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2311.06856 | Viacheslav Osaulenko | Viacheslav M. Osaulenko | On learning spatial sequences with the movement of attention | 10 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper we start with a simple question, how is it possible that humans
can recognize different movements over skin with only a prior visual experience
of them? Or in general, what is the representation of spatial sequences that
are invariant to scale, rotation, and translation across different modalities?
To answer, we rethink the mathematical representation of spatial sequences,
argue against the minimum description length principle, and focus on the
movements of attention. We advance the idea that spatial sequences must be
represented on different levels of abstraction, this adds redundancy but is
necessary for recognition and generalization. To address the open question of
how these abstractions are formed we propose two hypotheses: the first invites
exploring selectionism learning, instead of finding parameters in some models;
the second proposes to find new data structures, not neural network
architectures, to efficiently store and operate over redundant features to be
further selected. Movements of attention are central to human cognition and
lessons should be applied to new better learning algorithms.
| [
{
"version": "v1",
"created": "Sun, 12 Nov 2023 14:14:07 GMT"
}
] | 1,699,920,000,000 | [
[
"Osaulenko",
"Viacheslav M.",
""
]
] |
2311.07759 | Alessandro Oltramari | Alessandro Oltramari | Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | High-level reasoning can be defined as the capability to generalize over
knowledge acquired via experience, and to exhibit robust behavior in novel
situations. Such form of reasoning is a basic skill in humans, who seamlessly
use it in a broad spectrum of tasks, from language communication to decision
making in complex situations. When it manifests itself in understanding and
manipulating the everyday world of objects and their interactions, we talk
about common sense or commonsense reasoning. State-of-the-art AI systems don't
possess such capability: for instance, Large Language Models have recently
become popular by demonstrating remarkable fluency in conversing with humans,
but they still make trivial mistakes when probed for commonsense competence; on
a different level, performance degradation outside training data prevents
self-driving vehicles to safely adapt to unseen scenarios, a serious and
unsolved problem that limits the adoption of such technology. In this paper we
propose to enable high-level reasoning in AI systems by integrating cognitive
architectures with external neuro-symbolic components. We illustrate a hybrid
framework centered on ACT-R and we discuss the role of generative models in
recent and future applications.
| [
{
"version": "v1",
"created": "Mon, 13 Nov 2023 21:20:17 GMT"
}
] | 1,700,006,400,000 | [
[
"Oltramari",
"Alessandro",
""
]
] |
2311.08086 | Lanyue Tang | L. Tang, Y. Li, J. Yuan, A. Fu, J. Sun | CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and
Cognitive Theory | 15 pages, 31 figures, submitted to IEEE Transactions on Intelligent
Vehicles | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Active safety systems on vehicles often face problems with false alarms. Most
active safety systems predict the driver's trajectory with the assumption that
the driver is always in a normal emotion, and then infer risks. However, the
driver's trajectory uncertainty increases under abnormal emotions. This paper
proposes a new trajectory prediction model: CPSOR-GCN, which predicts vehicle
trajectories under abnormal emotions. At the physical level, the interaction
features between vehicles are extracted by the physical GCN module. At the
cognitive level, SOR cognitive theory is used as prior knowledge to build a
Dynamic Bayesian Network (DBN) structure. The conditional probability and state
transition probability of nodes from the calibrated SOR-DBN quantify the causal
relationship between cognitive factors, which is embedded into the cognitive
GCN module to extract the characteristics of the influence mechanism of
emotions on driving behavior. The CARLA-SUMO joint driving simulation platform
was built to develop dangerous pre-crash scenarios. Methods of recreating
traffic scenes were used to naturally induce abnormal emotions. The experiment
collected data from 26 participants to verify the proposed model. Compared with
the model that only considers physical motion features, the prediction accuracy
of the proposed model is increased by 68.70%. Furthermore,considering the
SOR-DBN reduces the prediction error of the trajectory by 15.93%. Compared with
other advanced trajectory prediction models, the results of CPSOR-GCN also have
lower errors. This model can be integrated into active safety systems to better
adapt to the driver's emotions, which could effectively reduce false alarms.
| [
{
"version": "v1",
"created": "Tue, 14 Nov 2023 11:13:00 GMT"
}
] | 1,700,006,400,000 | [
[
"Tang",
"L.",
""
],
[
"Li",
"Y.",
""
],
[
"Yuan",
"J.",
""
],
[
"Fu",
"A.",
""
],
[
"Sun",
"J.",
""
]
] |
2311.08547 | Arlindo Oliveira L | Arlindo L. Oliveira, Tiago Domingos, M\'ario Figueiredo, Pedro U. Lima | DeepThought: An Architecture for Autonomous Self-motivated Systems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The ability of large language models (LLMs) to engage in credible dialogues
with humans, taking into account the training data and the context of the
conversation, has raised discussions about their ability to exhibit intrinsic
motivations, agency, or even some degree of consciousness. We argue that the
internal architecture of LLMs and their finite and volatile state cannot
support any of these properties. By combining insights from complementary
learning systems, global neuronal workspace, and attention schema theories, we
propose to integrate LLMs and other deep learning systems into an architecture
for cognitive language agents able to exhibit properties akin to agency,
self-motivation, even some features of meta-cognition.
| [
{
"version": "v1",
"created": "Tue, 14 Nov 2023 21:20:23 GMT"
}
] | 1,700,092,800,000 | [
[
"Oliveira",
"Arlindo L.",
""
],
[
"Domingos",
"Tiago",
""
],
[
"Figueiredo",
"Mário",
""
],
[
"Lima",
"Pedro U.",
""
]
] |
2311.08760 | Hendrik Buschmeier | Hendrik Buschmeier, Heike M. Buhl, Friederike Kern, Angela Grimminger,
Helen Beierling, Josephine Fisher, Andr\'e Gro{\ss}, Ilona Horwath, Nils
Klowait, Stefan Lazarov, Michael Lenke, Vivien Lohmer, Katharina Rohlfing,
Ingrid Scharlau, Amit Singh, Lutz Terfloth, Anna-Lisa Vollmer, Yu Wang,
Annedore Wilmes, Britta Wrede | Forms of Understanding of XAI-Explanations | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Explainability has become an important topic in computer science and
artificial intelligence, leading to a subfield called Explainable Artificial
Intelligence (XAI). The goal of providing or seeking explanations is to achieve
(better) 'understanding' on the part of the explainee. However, what it means
to 'understand' is still not clearly defined, and the concept itself is rarely
the subject of scientific investigation. This conceptual article aims to
present a model of forms of understanding in the context of XAI and beyond.
From an interdisciplinary perspective bringing together computer science,
linguistics, sociology, and psychology, a definition of understanding and its
forms, assessment, and dynamics during the process of giving everyday
explanations are explored. Two types of understanding are considered as
possible outcomes of explanations, namely enabledness, 'knowing how' to do or
decide something, and comprehension, 'knowing that' -- both in different
degrees (from shallow to deep). Explanations regularly start with shallow
understanding in a specific domain and can lead to deep comprehension and
enabledness of the explanandum, which we see as a prerequisite for human users
to gain agency. In this process, the increase of comprehension and enabledness
are highly interdependent. Against the background of this systematization,
special challenges of understanding in XAI are discussed.
| [
{
"version": "v1",
"created": "Wed, 15 Nov 2023 08:06:51 GMT"
}
] | 1,700,092,800,000 | [
[
"Buschmeier",
"Hendrik",
""
],
[
"Buhl",
"Heike M.",
""
],
[
"Kern",
"Friederike",
""
],
[
"Grimminger",
"Angela",
""
],
[
"Beierling",
"Helen",
""
],
[
"Fisher",
"Josephine",
""
],
[
"Groß",
"André",
""
],
[
"Horwath",
"Ilona",
""
],
[
"Klowait",
"Nils",
""
],
[
"Lazarov",
"Stefan",
""
],
[
"Lenke",
"Michael",
""
],
[
"Lohmer",
"Vivien",
""
],
[
"Rohlfing",
"Katharina",
""
],
[
"Scharlau",
"Ingrid",
""
],
[
"Singh",
"Amit",
""
],
[
"Terfloth",
"Lutz",
""
],
[
"Vollmer",
"Anna-Lisa",
""
],
[
"Wang",
"Yu",
""
],
[
"Wilmes",
"Annedore",
""
],
[
"Wrede",
"Britta",
""
]
] |
2311.08834 | Duc Minh Vu | Ba Luat Le, Layla Martin, Emrah Demir, and Duc Minh Vu | A* search algorithm for an optimal investment problem in vehicle-sharing
systems | Full version of the conference paper which is accepted to be appear
in the proceeding of the The 12th International Conference on Computational
Data and Social Networks - SCONET2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We study an optimal investment problem that arises in the context of the
vehicle-sharing system. Given a set of locations to build stations, we need to
determine i) the sequence of stations to be built and the number of vehicles to
acquire in order to obtain the target state where all stations are built, and
ii) the number of vehicles to acquire and their allocation in order to maximize
the total profit returned by operating the system when some or all stations are
open. The profitability associated with operating open stations, measured over
a specific time period, is represented as a linear optimization problem applied
to a collection of open stations. With operating capital, the owner of the
system can open new stations. This property introduces a set-dependent aspect
to the duration required for opening a new station, and the optimal investment
problem can be viewed as a variant of the Traveling Salesman Problem (TSP) with
set-dependent cost. We propose an A* search algorithm to address this
particular variant of the TSP. Computational experiments highlight the benefits
of the proposed algorithm in comparison to the widely recognized Dijkstra
algorithm and propose future research to explore new possibilities and
applications for both exact and approximate A* algorithms.
| [
{
"version": "v1",
"created": "Wed, 15 Nov 2023 10:22:34 GMT"
}
] | 1,700,092,800,000 | [
[
"Le",
"Ba Luat",
""
],
[
"Martin",
"Layla",
""
],
[
"Demir",
"Emrah",
""
],
[
"Vu",
"Duc Minh",
""
]
] |
2311.08999 | Lena Nehale Ezzine | Morocco Solidarity Hackathon (Organizers, Speakers, Mentors and
Participant teams) | Leveraging AI for Natural Disaster Management : Takeaways From The
Moroccan Earthquake | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The devastating 6.8-magnitude earthquake in Al Haouz, Morocco in 2023
prompted critical reflections on global disaster management strategies,
resulting in a post-disaster hackathon, using artificial intelligence (AI) to
improve disaster preparedness, response, and recovery. This paper provides (i)
a comprehensive literature review, (ii) an overview of winning projects, (iii)
key insights and challenges, namely real-time open-source data, data scarcity,
and interdisciplinary collaboration barriers, and (iv) a community-call for
further action.
| [
{
"version": "v1",
"created": "Wed, 15 Nov 2023 14:38:41 GMT"
},
{
"version": "v2",
"created": "Sun, 10 Dec 2023 17:18:26 GMT"
}
] | 1,702,339,200,000 | [
[
"Hackathon",
"Morocco Solidarity",
"",
"Organizers, Speakers, Mentors and\n Participant teams"
]
] |
2311.09553 | Anubha Kabra | Anubha Kabra, Sanketh Rangreji, Yash Mathur, Aman Madaan, Emmy Liu,
Graham Neubig | Program-Aided Reasoners (better) Know What They Know | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Prior work shows that program-aided reasoning, in which large language models
(LLMs) are combined with programs written in programming languages such as
Python, can significantly improve accuracy on various reasoning tasks. However,
while accuracy is essential, it is also important for such reasoners to "know
what they know", which can be quantified through the calibration of the model.
In this paper, we compare the calibration of Program Aided Language Models
(PAL) and text-based Chain-of-thought (COT) prompting techniques over 5
datasets and 2 model types: LLaMA models and OpenAI models. Our results
indicate that PAL leads to improved calibration in 75% of the instances. Our
analysis uncovers that prompting styles that produce lesser diversity in
generations also have more calibrated results, and thus we also experiment with
inducing lower generation diversity using temperature scaling and find that for
certain temperatures, PAL is not only more accurate but is also more calibrated
than COT. Overall, we demonstrate that, in the majority of cases, program-aided
reasoners better know what they know than text-based counterparts.
| [
{
"version": "v1",
"created": "Thu, 16 Nov 2023 04:17:49 GMT"
}
] | 1,700,179,200,000 | [
[
"Kabra",
"Anubha",
""
],
[
"Rangreji",
"Sanketh",
""
],
[
"Mathur",
"Yash",
""
],
[
"Madaan",
"Aman",
""
],
[
"Liu",
"Emmy",
""
],
[
"Neubig",
"Graham",
""
]
] |
2311.09601 | Lajanugen Logeswaran | Lajanugen Logeswaran, Sungryull Sohn, Yiwei Lyu, Anthony Zhe Liu,
Dong-Ki Kim, Dongsub Shim, Moontae Lee, Honglak Lee | Code Models are Zero-shot Precondition Reasoners | Neurips Foundation Models for Decision Making Workshop 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the fundamental skills required for an agent acting in an environment
to complete tasks is the ability to understand what actions are plausible at
any given point. This work explores a novel use of code representations to
reason about action preconditions for sequential decision making tasks. Code
representations offer the flexibility to model procedural activities and
associated constraints as well as the ability to execute and verify constraint
satisfaction. Leveraging code representations, we extract action preconditions
from demonstration trajectories in a zero-shot manner using pre-trained code
models. Given these extracted preconditions, we propose a precondition-aware
action sampling strategy that ensures actions predicted by a policy are
consistent with preconditions. We demonstrate that the proposed approach
enhances the performance of few-shot policy learning approaches across
task-oriented dialog and embodied textworld benchmarks.
| [
{
"version": "v1",
"created": "Thu, 16 Nov 2023 06:19:27 GMT"
}
] | 1,700,179,200,000 | [
[
"Logeswaran",
"Lajanugen",
""
],
[
"Sohn",
"Sungryull",
""
],
[
"Lyu",
"Yiwei",
""
],
[
"Liu",
"Anthony Zhe",
""
],
[
"Kim",
"Dong-Ki",
""
],
[
"Shim",
"Dongsub",
""
],
[
"Lee",
"Moontae",
""
],
[
"Lee",
"Honglak",
""
]
] |
2311.09810 | EPTCS | Ashfaq Farooqui (Dependable Transport Systems, RISE Research
Institutes of Sweden, Bor{\aa}s, Sweden), Behrooz Sangchoolie (Dependable
Transport Systems, RISE Research Institutes of Sweden, Bor{\aa}s, Sweden) | Towards Formal Fault Injection for Safety Assessment of Automated
Systems | In Proceedings FMAS 2023, arXiv:2311.08987 | EPTCS 395, 2023, pp. 153-161 | 10.4204/EPTCS.395.11 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reasoning about safety, security, and other dependability attributes of
autonomous systems is a challenge that needs to be addressed before the
adoption of such systems in day-to-day life. Formal methods is a class of
methods that mathematically reason about a system's behavior. Thus, a
correctness proof is sufficient to conclude the system's dependability.
However, these methods are usually applied to abstract models of the system,
which might not fully represent the actual system. Fault injection, on the
other hand, is a testing method to evaluate the dependability of systems.
However, the amount of testing required to evaluate the system is rather large
and often a problem. This vision paper introduces formal fault injection, a
fusion of these two techniques throughout the development lifecycle to enhance
the dependability of autonomous systems. We advocate for a more cohesive
approach by identifying five areas of mutual support between formal methods and
fault injection. By forging stronger ties between the two fields, we pave the
way for developing safe and dependable autonomous systems. This paper delves
into the integration's potential and outlines future research avenues,
addressing open challenges along the way.
| [
{
"version": "v1",
"created": "Thu, 16 Nov 2023 11:34:18 GMT"
}
] | 1,700,179,200,000 | [
[
"Farooqui",
"Ashfaq",
"",
"Dependable Transport Systems, RISE Research\n Institutes of Sweden, Borås, Sweden"
],
[
"Sangchoolie",
"Behrooz",
"",
"Dependable\n Transport Systems, RISE Research Institutes of Sweden, Borås, Sweden"
]
] |
2311.10097 | Benji Alwis | Benji Alwis | Investigating AI's Challenges in Reasoning and Explanation from a
Historical Perspective | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper provides an overview of the intricate relationship between social
dynamics, technological advancements, and pioneering figures in the fields of
cybernetics and artificial intelligence. It explores the impact of
collaboration and interpersonal relationships among key scientists, such as
McCulloch, Wiener, Pitts, and Rosenblatt, on the development of cybernetics and
neural networks. It also discusses the contested attribution of credit for
important innovations like the backpropagation algorithm and the potential
consequences of unresolved debates within emerging scientific domains.
It emphasizes how interpretive flexibility, public perception, and the
influence of prominent figures can shape the trajectory of a new field. It
highlights the role of funding, media attention, and alliances in determining
the success and recognition of various research approaches. Additionally, it
points out the missed opportunities for collaboration and integration between
symbolic AI and neural network researchers, suggesting that a more unified
approach may be possible in today's era without the historical baggage of past
debates.
| [
{
"version": "v1",
"created": "Tue, 31 Oct 2023 12:31:32 GMT"
}
] | 1,700,438,400,000 | [
[
"Alwis",
"Benji",
""
]
] |
2311.10098 | Jonathan Ouwerx | Thomas Forster, Jonathan Ouwerx, Shak Ragoler | Automated Parliaments: A Solution to Decision Uncertainty and
Misalignment in Language Models | 39 pages, 4 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | As AI takes on a greater role in the modern world, it is essential to ensure
that AI models can overcome decision uncertainty and remain aligned with human
morality and interests. This research paper proposes a method for improving the
decision-making of language models (LMs) via Automated Parliaments (APs) -
constructs made of AI delegates each representing a certain perspective.
Delegates themselves consist of three AI models: generators, modifiers, and
evaluators. We specify two mechanisms for producing optimal solutions: the
Simultaneous Modification mechanism for response creation and an evaluation
mechanism for fairly assessing solutions. The overall process begins when each
generator creates a response aligned with its delegate's theory. The modifiers
alter all other responses to make them more self-aligned. The evaluators
collectively assess the best end response. Finally, the modifiers and
generators learn from feedback from the evaluators. In our research, we tested
the evaluation mechanism, comparing the use of single-value zero-shot prompting
and AP few-shot prompting in evaluating morally contentious scenarios. We found
that the AP architecture saw a 57.3% reduction in its loss value compared to
the baseline. We conclude by discussing some potential applications of APs and
specifically their potential impact when implemented as Automated Moral
Parliaments.
| [
{
"version": "v1",
"created": "Tue, 31 Oct 2023 17:44:04 GMT"
}
] | 1,700,438,400,000 | [
[
"Forster",
"Thomas",
""
],
[
"Ouwerx",
"Jonathan",
""
],
[
"Ragoler",
"Shak",
""
]
] |
2311.10104 | Amir Fayezioghani | Amir Fayezioghani | A Framework of Defining, Modeling, and Analyzing Cognition Mechanisms | A paper on cognition mechanisms as a basis for development of
foundational models/architectures of cognitive/intelligent systems | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Cognition is a core part of and a common topic among philosophy of mind,
psychology, neuroscience, AI, and cognitive science. Through a mechanistic
lens, I propose a framework of defining, modeling, and analyzing cognition
mechanisms. Firstly, appropriate terms are introduced and used in explanations
related to the framework and within the definition of a mechanism. I implicitly
contend that this terminology essentially characterizes a conceptual world
required for discussions in this paper. Secondly, a mathematical model of a
mechanism based on directed graphs is proposed. Thirdly, the definition of a
base necessary for a mechanism to be classified as a cognition mechanism is
proposed. I argue that the cognition base has the features of the cognition
self of humans. Fourthly, three ways to mechanistically look at mechanisms is
defined and specific instances of them are suggested. Fifthly, standards for
visualization and presentation of mechanisms, cognition mechanisms, and the
instances to mechanistically look at them are suggested and used to analyze
cognition mechanisms through appropriate examples. Finally, the features of
this paper are discussed and prospects of further development of the proposed
framework are briefly expressed.
| [
{
"version": "v1",
"created": "Mon, 13 Nov 2023 12:31:46 GMT"
}
] | 1,700,438,400,000 | [
[
"Fayezioghani",
"Amir",
""
]
] |
2311.10538 | Silen Naihin | Silen Naihin, David Atkinson, Marc Green, Merwane Hamadi, Craig Swift,
Douglas Schonholtz, Adam Tauman Kalai, David Bau | Testing Language Model Agents Safely in the Wild | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet
real-world autonomous tests face several unique safety challenges, both due to
the possibility of causing harm during a test, as well as the risk of
encountering new unsafe agent behavior through interactions with real-world and
potentially malicious actors. We propose a framework for conducting safe
autonomous agent tests on the open internet: agent actions are audited by a
context-sensitive monitor that enforces a stringent safety boundary to stop an
unsafe test, with suspect behavior ranked and logged to be examined by humans.
We design a basic safety monitor (AgentMonitor) that is flexible enough to
monitor existing LLM agents, and, using an adversarial simulated agent, we
measure its ability to identify and stop unsafe situations. Then we apply the
AgentMonitor on a battery of real-world tests of AutoGPT, and we identify
several limitations and challenges that will face the creation of safe
in-the-wild tests as autonomous agents grow more capable.
| [
{
"version": "v1",
"created": "Fri, 17 Nov 2023 14:06:05 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Nov 2023 22:54:49 GMT"
},
{
"version": "v3",
"created": "Sun, 3 Dec 2023 13:18:09 GMT"
}
] | 1,701,734,400,000 | [
[
"Naihin",
"Silen",
""
],
[
"Atkinson",
"David",
""
],
[
"Green",
"Marc",
""
],
[
"Hamadi",
"Merwane",
""
],
[
"Swift",
"Craig",
""
],
[
"Schonholtz",
"Douglas",
""
],
[
"Kalai",
"Adam Tauman",
""
],
[
"Bau",
"David",
""
]
] |
2311.10809 | Yao-Shun Chuang | Yao-Shun Chuang, Chun-Teh Lee, Ryan Brandon, Trung Duong Tran,
Oluwabunmi Tokede, Muhammad F. Walji, Xiaoqian Jiang | Extracting periodontitis diagnosis in clinical notes with RoBERTa and
regular expression | IEEE ICHI 2023, see https://ieeeichi.github.io/ICHI2023/program.html | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study aimed to utilize text processing and natural language processing
(NLP) models to mine clinical notes for the diagnosis of periodontitis and to
evaluate the performance of a named entity recognition (NER) model on different
regular expression (RE) methods. Two complexity levels of RE methods were used
to extract and generate the training data. The SpaCy package and RoBERTa
transformer models were used to build the NER model and evaluate its
performance with the manual-labeled gold standards. The comparison of the RE
methods with the gold standard showed that as the complexity increased in the
RE algorithms, the F1 score increased from 0.3-0.4 to around 0.9. The NER
models demonstrated excellent predictions, with the simple RE method showing
0.84-0.92 in the evaluation metrics, and the advanced and combined RE method
demonstrating 0.95-0.99 in the evaluation. This study provided an example of
the benefit of combining NER methods and NLP models in extracting target
information from free-text to structured data and fulfilling the need for
missing diagnoses from unstructured notes.
| [
{
"version": "v1",
"created": "Fri, 17 Nov 2023 18:09:21 GMT"
}
] | 1,700,524,800,000 | [
[
"Chuang",
"Yao-Shun",
""
],
[
"Lee",
"Chun-Teh",
""
],
[
"Brandon",
"Ryan",
""
],
[
"Tran",
"Trung Duong",
""
],
[
"Tokede",
"Oluwabunmi",
""
],
[
"Walji",
"Muhammad F.",
""
],
[
"Jiang",
"Xiaoqian",
""
]
] |
2311.10840 | Vikash Gupta | Barbaros Selnur Erdal, Vikash Gupta, Mutlu Demirer, Kim H. Fair,
Richard D. White, Jeff Blair, Barbara Deichert, Laurie Lafleur, Ming Melvin
Qin, David Bericat, Brad Genereaux | Integration and Implementation Strategies for AI Algorithm Deployment
with Smart Routing Rules and Workflow Management | 13 pages, 6 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | This paper reviews the challenges hindering the widespread adoption of
artificial intelligence (AI) solutions in the healthcare industry, focusing on
computer vision applications for medical imaging, and how interoperability and
enterprise-grade scalability can be used to address these challenges. The
complex nature of healthcare workflows, intricacies in managing large and
secure medical imaging data, and the absence of standardized frameworks for AI
development pose significant barriers and require a new paradigm to address
them.
The role of interoperability is examined in this paper as a crucial factor in
connecting disparate applications within healthcare workflows. Standards such
as DICOM, Health Level 7 (HL7), and Integrating the Healthcare Enterprise (IHE)
are highlighted as foundational for common imaging workflows. A specific focus
is placed on the role of DICOM gateways, with Smart Routing Rules and Workflow
Management leading transformational efforts in this area.
To drive enterprise scalability, new tools are needed. Project MONAI,
established in 2019, is introduced as an initiative aiming to redefine the
development of medical AI applications. The MONAI Deploy App SDK, a component
of Project MONAI, is identified as a key tool in simplifying the packaging and
deployment process, enabling repeatable, scalable, and standardized deployment
patterns for AI applications.
The abstract underscores the potential impact of successful AI adoption in
healthcare, offering physicians both life-saving and time-saving insights and
driving efficiencies in radiology department workflows. The collaborative
efforts between academia and industry, are emphasized as essential for
advancing the adoption of healthcare AI solutions.
| [
{
"version": "v1",
"created": "Fri, 17 Nov 2023 19:38:37 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Nov 2023 19:41:07 GMT"
}
] | 1,700,697,600,000 | [
[
"Erdal",
"Barbaros Selnur",
""
],
[
"Gupta",
"Vikash",
""
],
[
"Demirer",
"Mutlu",
""
],
[
"Fair",
"Kim H.",
""
],
[
"White",
"Richard D.",
""
],
[
"Blair",
"Jeff",
""
],
[
"Deichert",
"Barbara",
""
],
[
"Lafleur",
"Laurie",
""
],
[
"Qin",
"Ming Melvin",
""
],
[
"Bericat",
"David",
""
],
[
"Genereaux",
"Brad",
""
]
] |
2311.10856 | Warren Del-Pinto | Warren Del-Pinto, George Demetriou, Meghna Jani, Rikesh Patel, Leanne
Gray, Alex Bulcock, Niels Peek, Andrew S. Kanter, William G Dixon, Goran
Nenadic | Exploring the Consistency, Quality and Challenges in Manual and
Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Coding of unstructured clinical free-text to produce interoperable structured
data is essential to improve direct care, support clinical communication and to
enable clinical research.However, manual clinical coding is difficult and time
consuming, which motivates the development and use of natural language
processing for automated coding. This work evaluates the quality and
consistency of both manual and automated clinical coding of diagnoses from
hospital outpatient letters. Using 100 randomly selected letters, two human
clinicians performed coding of diagnosis lists to SNOMED CT. Automated coding
was also performed using IMO's Concept Tagger. A gold standard was constructed
by a panel of clinicians from a subset of the annotated diagnoses. This was
used to evaluate the quality and consistency of both manual and automated
coding via (1) a distance-based metric, treating SNOMED CT as a graph, and (2)
a qualitative metric agreed upon by the panel of clinicians. Correlation
between the two metrics was also evaluated. Comparing human and
computer-generated codes to the gold standard, the results indicate that humans
slightly out-performed automated coding, while both performed notably better
when there was only a single diagnosis contained in the free-text description.
Automated coding was considered acceptable by the panel of clinicians in
approximately 90% of cases.
| [
{
"version": "v1",
"created": "Fri, 17 Nov 2023 20:32:24 GMT"
}
] | 1,700,524,800,000 | [
[
"Del-Pinto",
"Warren",
""
],
[
"Demetriou",
"George",
""
],
[
"Jani",
"Meghna",
""
],
[
"Patel",
"Rikesh",
""
],
[
"Gray",
"Leanne",
""
],
[
"Bulcock",
"Alex",
""
],
[
"Peek",
"Niels",
""
],
[
"Kanter",
"Andrew S.",
""
],
[
"Dixon",
"William G",
""
],
[
"Nenadic",
"Goran",
""
]
] |
2311.10932 | David Thorstad | David Thorstad | Cognitive bias in large language models: Cautious optimism meets
anti-Panglossian meliorism | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Traditional discussions of bias in large language models focus on a
conception of bias closely tied to unfairness, especially as affecting
marginalized groups. Recent work raises the novel possibility of assessing the
outputs of large language models for a range of cognitive biases familiar from
research in judgment and decisionmaking. My aim in this paper is to draw two
lessons from recent discussions of cognitive bias in large language models:
cautious optimism about the prevalence of bias in current models coupled with
an anti-Panglossian willingness to concede the existence of some genuine biases
and work to reduce them. I draw out philosophical implications of this
discussion for the rationality of human cognitive biases as well as the role of
unrepresentative data in driving model biases.
| [
{
"version": "v1",
"created": "Sat, 18 Nov 2023 01:58:23 GMT"
}
] | 1,700,524,800,000 | [
[
"Thorstad",
"David",
""
]
] |
2311.10940 | Simi Haber | Simi Haber, Yonatan Wexler | Unsupervised Estimation of Ensemble Accuracy | 4 pages, 2 figures. Accepted to InfoCOG@NeurIPS 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Ensemble learning combines several individual models to obtain a better
generalization performance. In this work we present a practical method for
estimating the joint power of several classifiers. It differs from existing
approaches which focus on "diversity" measures by not relying on labels. This
makes it both accurate and practical in the modern setting of unsupervised
learning with huge datasets.
The heart of the method is a combinatorial bound on the number of mistakes
the ensemble is likely to make. The bound can be efficiently approximated in
time linear in the number of samples. We relate the bound to actual
misclassifications, hence its usefulness as a predictor of performance.
We demonstrate the method on popular large-scale face recognition datasets
which provide a useful playground for fine-grain classification tasks using
noisy data over many classes.
| [
{
"version": "v1",
"created": "Sat, 18 Nov 2023 02:31:36 GMT"
},
{
"version": "v2",
"created": "Wed, 20 Dec 2023 21:55:17 GMT"
}
] | 1,703,203,200,000 | [
[
"Haber",
"Simi",
""
],
[
"Wexler",
"Yonatan",
""
]
] |
2311.11045 | Arindam Mitra | Arindam Mitra, Luciano Del Corro, Shweti Mahajan, Andres Codas,
Clarisse Simoes, Sahaj Agarwal, Xuxi Chen, Anastasia Razdaibiedina, Erik
Jones, Kriti Aggarwal, Hamid Palangi, Guoqing Zheng, Corby Rosset, Hamed
Khanpour, Ahmed Awadallah | Orca 2: Teaching Small Language Models How to Reason | Added url to model weights fixed typo in Author name | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Orca 1 learns from rich signals, such as explanation traces, allowing it to
outperform conventional instruction-tuned models on benchmarks like BigBench
Hard and AGIEval. In Orca 2, we continue exploring how improved training
signals can enhance smaller LMs' reasoning abilities. Research on training
small LMs has often relied on imitation learning to replicate the output of
more capable models. We contend that excessive emphasis on imitation may
restrict the potential of smaller models. We seek to teach small LMs to employ
different solution strategies for different tasks, potentially different from
the one used by the larger model. For example, while larger models might
provide a direct answer to a complex task, smaller models may not have the same
capacity. In Orca 2, we teach the model various reasoning techniques
(step-by-step, recall then generate, recall-reason-generate, direct answer,
etc.). More crucially, we aim to help the model learn to determine the most
effective solution strategy for each task. We evaluate Orca 2 using a
comprehensive set of 15 diverse benchmarks (corresponding to approximately 100
tasks and over 36,000 unique prompts). Orca 2 significantly surpasses models of
similar size and attains performance levels similar or better to those of
models 5-10x larger, as assessed on complex tasks that test advanced reasoning
abilities in zero-shot settings. make Orca 2 weights publicly available at
aka.ms/orca-lm to support research on the development, evaluation, and
alignment of smaller LMs
| [
{
"version": "v1",
"created": "Sat, 18 Nov 2023 11:44:52 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Nov 2023 19:43:31 GMT"
}
] | 1,700,697,600,000 | [
[
"Mitra",
"Arindam",
""
],
[
"Del Corro",
"Luciano",
""
],
[
"Mahajan",
"Shweti",
""
],
[
"Codas",
"Andres",
""
],
[
"Simoes",
"Clarisse",
""
],
[
"Agarwal",
"Sahaj",
""
],
[
"Chen",
"Xuxi",
""
],
[
"Razdaibiedina",
"Anastasia",
""
],
[
"Jones",
"Erik",
""
],
[
"Aggarwal",
"Kriti",
""
],
[
"Palangi",
"Hamid",
""
],
[
"Zheng",
"Guoqing",
""
],
[
"Rosset",
"Corby",
""
],
[
"Khanpour",
"Hamed",
""
],
[
"Awadallah",
"Ahmed",
""
]
] |
2311.11120 | Boyang Deng | Boyang Deng, Xin Wen, and Zhan Gao | An Improved Neural Network Model Based On CNN Using For Fruit Sugar
Degree Detection | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Artificial Intelligence(AI) widely applies in Image Classification and
Recognition, Text Understanding and Natural Language Processing, which makes
great progress. In this paper, we introduced AI into the fruit quality
detection field. We designed a fruit sugar degree regression model using an
Artificial Neural Network based on spectra of fruits within the
visible/near-infrared(V/NIR)range. After analysis of fruit spectra, we
innovatively proposed a new neural network structure: low layers consist of a
Multilayer Perceptron(MLP), a middle layer is a 2-dimensional correlation
matrix layer, and high layers consist of several Convolutional Neural
Network(CNN) layers. In this study, we used fruit sugar value as a detection
target, collecting two fruits called Gan Nan Navel and Tian Shan Pear as
samples, doing experiments respectively, and comparing their results. We used
Analysis of Variance(ANOVA) to evaluate the reliability of the dataset we
collected. Then, we tried multiple strategies to process spectrum data,
evaluating their effects. In this paper, we tried to add Wavelet
Decomposition(WD) to reduce feature dimensions and a Genetic Algorithm(GA) to
find excellent features. Then, we compared Neural Network models with
traditional Partial Least Squares(PLS) based models. We also compared the
neural network structure we designed(MLP-CNN) with other traditional neural
network structures. In this paper, we proposed a new evaluation standard
derived from dataset standard deviation(STD) for evaluating detection
performance, validating the viability of using an artificial neural network
model to do fruit sugar degree nondestructive detection.
| [
{
"version": "v1",
"created": "Sat, 18 Nov 2023 17:07:25 GMT"
}
] | 1,700,524,800,000 | [
[
"Deng",
"Boyang",
""
],
[
"Wen",
"Xin",
""
],
[
"Gao",
"Zhan",
""
]
] |
2311.11175 | Eduardo C. Garrido-Merch\'an | Eduardo C. Garrido-Merchan | Best uses of ChatGPT and Generative AI for computer science research | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generative Artificial Intelligence (AI), particularly tools like OpenAI's
popular ChatGPT, is reshaping the landscape of computer science research. Used
wisely, these tools can boost the productivity of a computer research
scientist. This paper provides an exploration of the diverse applications of
ChatGPT and other generative AI technologies in computer science academic
research, making recommendations about the use of Generative AI to make more
productive the role of the computer research scientist, with the focus of
writing new research papers. We highlight innovative uses such as brainstorming
research ideas, aiding in the drafting and styling of academic papers and
assisting in the synthesis of state-of-the-art section. Further, we delve into
using these technologies in understanding interdisciplinary approaches, making
complex texts simpler, and recommending suitable academic journals for
publication. Significant focus is placed on generative AI's contributions to
synthetic data creation, research methodology, and mentorship, as well as in
task organization and article quality assessment. The paper also addresses the
utility of AI in article review, adapting texts to length constraints,
constructing counterarguments, and survey development. Moreover, we explore the
capabilities of these tools in disseminating ideas, generating images and
audio, text transcription, and engaging with editors. We also describe some
non-recommended uses of generative AI for computer science research, mainly
because of the limitations of this technology.
| [
{
"version": "v1",
"created": "Sat, 18 Nov 2023 21:57:54 GMT"
}
] | 1,700,524,800,000 | [
[
"Garrido-Merchan",
"Eduardo C.",
""
]
] |
2311.11211 | Gongbo Zhang | Gongbo Zhang, Qiao Jin, Denis Jered McInerney, Yong Chen, Fei Wang,
Curtis L. Cole, Qian Yang, Yanshan Wang, Bradley A. Malin, Mor Peleg, Byron
C. Wallace, Zhiyong Lu, Chunhua Weng, Yifan Peng | Leveraging Generative AI for Clinical Evidence Summarization Needs to
Ensure Trustworthiness | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Evidence-based medicine promises to improve the quality of healthcare by
empowering medical decisions and practices with the best available evidence.
The rapid growth of medical evidence, which can be obtained from various
sources, poses a challenge in collecting, appraising, and synthesizing the
evidential information. Recent advancements in generative AI, exemplified by
large language models, hold promise in facilitating the arduous task. However,
developing accountable, fair, and inclusive models remains a complicated
undertaking. In this perspective, we discuss the trustworthiness of generative
AI in the context of automated summarization of medical evidence.
| [
{
"version": "v1",
"created": "Sun, 19 Nov 2023 03:29:45 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Jan 2024 12:20:34 GMT"
},
{
"version": "v3",
"created": "Mon, 1 Apr 2024 02:04:25 GMT"
}
] | 1,712,016,000,000 | [
[
"Zhang",
"Gongbo",
""
],
[
"Jin",
"Qiao",
""
],
[
"McInerney",
"Denis Jered",
""
],
[
"Chen",
"Yong",
""
],
[
"Wang",
"Fei",
""
],
[
"Cole",
"Curtis L.",
""
],
[
"Yang",
"Qian",
""
],
[
"Wang",
"Yanshan",
""
],
[
"Malin",
"Bradley A.",
""
],
[
"Peleg",
"Mor",
""
],
[
"Wallace",
"Byron C.",
""
],
[
"Lu",
"Zhiyong",
""
],
[
"Weng",
"Chunhua",
""
],
[
"Peng",
"Yifan",
""
]
] |
2311.11237 | Jiazhen Wang | Jiazhen Wang | Implementation of AI Deep Learning Algorithm For Multi-Modal Sentiment
Analysis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A multi-modal emotion recognition method was established by combining
two-channel convolutional neural network with ring network. This method can
extract emotional information effectively and improve learning efficiency. The
words were vectorized with GloVe, and the word vector was input into the
convolutional neural network. Combining attention mechanism and maximum pool
converter BiSRU channel, the local deep emotion and pre-post sequential emotion
semantics are obtained. Finally, multiple features are fused and input as the
polarity of emotion, so as to achieve the emotion analysis of the target.
Experiments show that the emotion analysis method based on feature fusion can
effectively improve the recognition accuracy of emotion data set and reduce the
learning time. The model has a certain generalization.
| [
{
"version": "v1",
"created": "Sun, 19 Nov 2023 05:49:39 GMT"
}
] | 1,700,524,800,000 | [
[
"Wang",
"Jiazhen",
""
]
] |
2311.11250 | Sudhanshu Kumar Mr. | Sudhanshu Kumar (1), Partha Pratim Roy (1), Debi Prosad Dogra (2),
Byung-Gyu Kim (3) ((1) Department of Computer Science and Engineering, IIT
Roorkee, India, (2) School of Electrical Sciences, IIT Bhubaneswar, Odisha,
India, (3) Department of IT Engineering, Sookmyung Women's University, Seoul,
South Korea) | A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and
Applications | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sentiment analysis (SA) is an emerging field in text mining. It is the
process of computationally identifying and categorizing opinions expressed in a
piece of text over different social media platforms. Social media plays an
essential role in knowing the customer mindset towards a product, services, and
the latest market trends. Most organizations depend on the customer's response
and feedback to upgrade their offered products and services. SA or opinion
mining seems to be a promising research area for various domains. It plays a
vital role in analyzing big data generated daily in structured and unstructured
formats over the internet. This survey paper defines sentiment and its recent
research and development in different domains, including voice, images, videos,
and text. The challenges and opportunities of sentiment analysis are also
discussed in the paper.
\keywords{Sentiment Analysis, Machine Learning, Lexicon-based approach, Deep
Learning, Natural Language Processing}
| [
{
"version": "v1",
"created": "Sun, 19 Nov 2023 06:29:41 GMT"
}
] | 1,700,524,800,000 | [
[
"Kumar",
"Sudhanshu",
""
],
[
"Roy",
"Partha Pratim",
""
],
[
"Dogra",
"Debi Prosad",
""
],
[
"Kim",
"Byung-Gyu",
""
]
] |
2311.11288 | Zuzanna Osika | Zuzanna Osika, Jazmin Zatarain Salazar, Diederik M. Roijers, Frans A.
Oliehoek and Pradeep K. Murukannaiah | What Lies beyond the Pareto Front? A Survey on Decision-Support Methods
for Multi-Objective Optimization | IJCAI 2023 Conference Paper, Survey Track | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present a review that unifies decision-support methods for exploring the
solutions produced by multi-objective optimization (MOO) algorithms. As MOO is
applied to solve diverse problems, approaches for analyzing the trade-offs
offered by MOO algorithms are scattered across fields. We provide an overview
of the advances on this topic, including methods for visualization, mining the
solution set, and uncertainty exploration as well as emerging research
directions, including interactivity, explainability, and ethics. We synthesize
these methods drawing from different fields of research to build a unified
approach, independent of the application. Our goals are to reduce the entry
barrier for researchers and practitioners on using MOO algorithms and to
provide novel research directions.
| [
{
"version": "v1",
"created": "Sun, 19 Nov 2023 10:24:39 GMT"
}
] | 1,700,524,800,000 | [
[
"Osika",
"Zuzanna",
""
],
[
"Salazar",
"Jazmin Zatarain",
""
],
[
"Roijers",
"Diederik M.",
""
],
[
"Oliehoek",
"Frans A.",
""
],
[
"Murukannaiah",
"Pradeep K.",
""
]
] |
2311.11315 | Jingqing Ruan | Yilun Kong, Jingqing Ruan, Yihong Chen, Bin Zhang, Tianpeng Bao,
Shiwei Shi, Guoqing Du, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui
Zhao | TPTU-v2: Boosting Task Planning and Tool Usage of Large Language
Model-based Agents in Real-world Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have demonstrated proficiency in addressing
tasks that necessitate a combination of task planning and the usage of external
tools that require a blend of task planning and the utilization of external
tools, such as APIs. However, real-world complex systems present three
prevalent challenges concerning task planning and tool usage: (1) The real
system usually has a vast array of APIs, so it is impossible to feed the
descriptions of all APIs to the prompt of LLMs as the token length is limited;
(2) the real system is designed for handling complex tasks, and the base LLMs
can hardly plan a correct sub-task order and API-calling order for such tasks;
(3) Similar semantics and functionalities among APIs in real systems create
challenges for both LLMs and even humans in distinguishing between them. In
response, this paper introduces a comprehensive framework aimed at enhancing
the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents operating
within real-world systems. Our framework comprises three key components
designed to address these challenges: (1) the API Retriever selects the most
pertinent APIs for the user task among the extensive array available; (2) LLM
Finetuner tunes a base LLM so that the finetuned LLM can be more capable for
task planning and API calling; (3) the Demo Selector adaptively retrieves
different demonstrations related to hard-to-distinguish APIs, which is further
used for in-context learning to boost the final performance. We validate our
methods using a real-world commercial system as well as an open-sourced
academic dataset, and the outcomes clearly showcase the efficacy of each
individual component as well as the integrated framework.
| [
{
"version": "v1",
"created": "Sun, 19 Nov 2023 12:37:30 GMT"
}
] | 1,700,524,800,000 | [
[
"Kong",
"Yilun",
""
],
[
"Ruan",
"Jingqing",
""
],
[
"Chen",
"Yihong",
""
],
[
"Zhang",
"Bin",
""
],
[
"Bao",
"Tianpeng",
""
],
[
"Shi",
"Shiwei",
""
],
[
"Du",
"Guoqing",
""
],
[
"Hu",
"Xiaoru",
""
],
[
"Mao",
"Hangyu",
""
],
[
"Li",
"Ziyue",
""
],
[
"Zeng",
"Xingyu",
""
],
[
"Zhao",
"Rui",
""
]
] |
2311.11476 | Shah Miah Prof | Rashikala Weerawarna and Shah J Miah | Empowering remittance management in the digitised landscape: A real-time
Data-Driven Decision Support with predictive abilities for financial
transactions | Ppaper has been accepted for presenting in the Australasian
Conference on Information Systems 2023, Dec 6 to 8, Wellington, NZ | Australasian Conference on Information Systems 2023, Wellington,
NZ | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The advent of Blockchain technology (BT) revolutionised the way remittance
transactions are recorded. Banks and remittance organisations have shown a
growing interest in exploring blockchain's potential advantages over
traditional practices. This paper presents a data-driven predictive decision
support approach as an innovative artefact designed for the blockchain-oriented
remittance industry. Employing a theory-generating Design Science Research
(DSR) approach, we have uncovered the emergence of predictive capabilities
driven by transactional big data. The artefact integrates predictive analytics
and Machine Learning (ML) to enable real-time remittance monitoring, empowering
management decision-makers to address challenges in the uncertain digitised
landscape of blockchain-oriented remittance companies. Bridging the gap between
theory and practice, this research not only enhances the security of the
remittance ecosystem but also lays the foundation for future predictive
decision support solutions, extending the potential of predictive analytics to
other domains. Additionally, the generated theory from the artifact's
implementation enriches the DSR approach and fosters grounded and stakeholder
theory development in the information systems domain.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 01:04:04 GMT"
}
] | 1,700,524,800,000 | [
[
"Weerawarna",
"Rashikala",
""
],
[
"Miah",
"Shah J",
""
]
] |
2311.11539 | Jing Yang | Jing Yang, Wei Su | A New Approach to Intuitionistic Fuzzy Decision Making Based on
Projection Technology and Cosine Similarity Measure | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For a multi-attribute decision making (MADM) problem, the information of
alternatives under different attributes is given in the form of intuitionistic
fuzzy number(IFN). Intuitionistic fuzzy set (IFS) plays an important role in
dealing with un-certain and incomplete information. The similarity measure of
intuitionistic fuzzy sets (IFSs) has always been a research hotspot. A new
similarity measure of IFSs based on the projection technology and cosine
similarity measure, which con-siders the direction and length of IFSs at the
same time, is first proposed in this paper. The objective of the presented
pa-per is to develop a MADM method and medical diagnosis method under IFS using
the projection technology and cosine similarity measure. Some examples are used
to illustrate the comparison results of the proposed algorithm and some
exist-ing methods. The comparison result shows that the proposed algorithm is
effective and can identify the optimal scheme accurately. In medical diagnosis
area, it can be used to quickly diagnose disease. The proposed method enriches
the exist-ing similarity measure methods and it can be applied to not only
IFSs, but also other interval-valued intuitionistic fuzzy sets(IVIFSs) as well.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 05:07:03 GMT"
}
] | 1,700,524,800,000 | [
[
"Yang",
"Jing",
""
],
[
"Su",
"Wei",
""
]
] |
2311.11542 | Izack Cohen | Izack Cohen | Data-driven project planning: An integrated network learning and
constraint relaxation approach in favor of scheduling | null | null | 10.1109/TEM.2024.3382727 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our focus is on projects, i.e., business processes, which are emerging as the
economic drivers of our times. Differently from day-to-day operational
processes that do not require detailed planning, a project requires planning
and resource-constrained scheduling for coordinating resources across sub- or
related projects and organizations. A planner in charge of project planning has
to select a set of activities to perform, determine their precedence
constraints, and schedule them according to temporal project constraints. We
suggest a data-driven project planning approach for classes of projects such as
infrastructure building and information systems development projects. A project
network is first learned from historical records. The discovered network
relaxes temporal constraints embedded in individual projects, thus uncovering
where planning and scheduling flexibility can be exploited for greater benefit.
Then, the network, which contains multiple project plan variations, from which
one has to be selected, is enriched by identifying decision rules and frequent
paths. The planner can rely on the project network for: 1) decoding a project
variation such that it forms a new project plan, and 2) applying
resource-constrained project scheduling procedures to determine the project's
schedule and resource allocation. Using two real-world project datasets, we
show that the suggested approach may provide the planner with significant
flexibility (up to a 26% reduction of the critical path of a real project) to
adjust the project plan and schedule. We believe that the proposed approach can
play an important part in supporting decision making towards automated
data-driven project planning.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 05:13:17 GMT"
},
{
"version": "v2",
"created": "Sun, 7 Apr 2024 12:22:45 GMT"
}
] | 1,712,620,800,000 | [
[
"Cohen",
"Izack",
""
]
] |
2311.11652 | Sha Wang | Sha Wang, Yuchen Li, Hanhua Xiao, Lambert Deng, Yanfei Dong | Web News Timeline Generation with Extended Task Prompting | 4 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The creation of news timeline is essential for a comprehensive and contextual
understanding of events as they unfold over time. This approach aids in
discerning patterns and trends that might be obscured when news is viewed in
isolation. By organizing news in a chronological sequence, it becomes easier to
track the development of stories, understand the interrelation of events, and
grasp the broader implications of news items. This is particularly helpful in
sectors like finance and insurance, where timely understanding of the event
development-ranging from extreme weather to political upheavals and health
crises-is indispensable for effective risk management. While traditional
natural language processing (NLP) techniques have had some success, they often
fail to capture the news with nuanced relevance that are readily apparent to
domain experts, hindering broader industry integration. The advance of Large
Language Models (LLMs) offers a renewed opportunity to tackle this challenge.
However, direct prompting LLMs for this task is often ineffective. Our study
investigates the application of an extended task prompting technique to assess
past news relevance. We demonstrate that enhancing conventional prompts with
additional tasks boosts their effectiveness on various news dataset, rendering
news timeline generation practical for professional use. This work has been
deployed as a publicly accessible browser extension which is adopted within our
network.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 10:38:22 GMT"
}
] | 1,700,524,800,000 | [
[
"Wang",
"Sha",
""
],
[
"Li",
"Yuchen",
""
],
[
"Xiao",
"Hanhua",
""
],
[
"Deng",
"Lambert",
""
],
[
"Dong",
"Yanfei",
""
]
] |
2311.11689 | Derui Lyu | Taiyu Ban and Lyuzhou Chen and Derui Lyu and Xiangyu Wang and Huanhuan
Chen | Causal Structure Learning Supervised by Large Language Model | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causal discovery from observational data is pivotal for deciphering complex
relationships. Causal Structure Learning (CSL), which focuses on deriving
causal Directed Acyclic Graphs (DAGs) from data, faces challenges due to vast
DAG spaces and data sparsity. The integration of Large Language Models (LLMs),
recognized for their causal reasoning capabilities, offers a promising
direction to enhance CSL by infusing it with knowledge-based causal inferences.
However, existing approaches utilizing LLMs for CSL have encountered issues,
including unreliable constraints from imperfect LLM inferences and the
computational intensity of full pairwise variable analyses. In response, we
introduce the Iterative LLM Supervised CSL (ILS-CSL) framework. ILS-CSL
innovatively integrates LLM-based causal inference with CSL in an iterative
process, refining the causal DAG using feedback from LLMs. This method not only
utilizes LLM resources more efficiently but also generates more robust and
high-quality structural constraints compared to previous methodologies. Our
comprehensive evaluation across eight real-world datasets demonstrates
ILS-CSL's superior performance, setting a new standard in CSL efficacy and
showcasing its potential to significantly advance the field of causal
discovery. The codes are available at
\url{https://github.com/tyMadara/ILS-CSL}.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 11:43:20 GMT"
}
] | 1,700,524,800,000 | [
[
"Ban",
"Taiyu",
""
],
[
"Chen",
"Lyuzhou",
""
],
[
"Lyu",
"Derui",
""
],
[
"Wang",
"Xiangyu",
""
],
[
"Chen",
"Huanhuan",
""
]
] |
2311.11756 | Xuechao Wang | Xuechao Wang, Junqing Huang, Sven Nomm, Marianna Chatzakou, Kadri
Medijainen, Aaro Toomela, Michael Ruzhansky | LSTM-CNN: An efficient diagnostic network for Parkinson's disease
utilizing dynamic handwriting analysis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background and objectives: Dynamic handwriting analysis, due to its
non-invasive and readily accessible nature, has recently emerged as a vital
adjunctive method for the early diagnosis of Parkinson's disease. In this
study, we design a compact and efficient network architecture to analyse the
distinctive handwriting patterns of patients' dynamic handwriting signals,
thereby providing an objective identification for the Parkinson's disease
diagnosis.
Methods: The proposed network is based on a hybrid deep learning approach
that fully leverages the advantages of both long short-term memory (LSTM) and
convolutional neural networks (CNNs). Specifically, the LSTM block is adopted
to extract the time-varying features, while the CNN-based block is implemented
using one-dimensional convolution for low computational cost. Moreover, the
hybrid model architecture is continuously refined under ablation studies for
superior performance. Finally, we evaluate the proposed method with its
generalization under a five-fold cross-validation, which validates its
efficiency and robustness.
Results: The proposed network demonstrates its versatility by achieving
impressive classification accuracies on both our new DraWritePD dataset
($96.2\%$) and the well-established PaHaW dataset ($90.7\%$). Moreover, the
network architecture also stands out for its excellent lightweight design,
occupying a mere $0.084$M of parameters, with a total of only $0.59$M
floating-point operations. It also exhibits near real-time CPU inference
performance, with inference times ranging from $0.106$ to $0.220$s.
Conclusions: We present a series of experiments with extensive analysis,
which systematically demonstrate the effectiveness and efficiency of the
proposed hybrid neural network in extracting distinctive handwriting patterns
for precise diagnosis of Parkinson's disease.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 13:34:08 GMT"
}
] | 1,700,524,800,000 | [
[
"Wang",
"Xuechao",
""
],
[
"Huang",
"Junqing",
""
],
[
"Nomm",
"Sven",
""
],
[
"Chatzakou",
"Marianna",
""
],
[
"Medijainen",
"Kadri",
""
],
[
"Toomela",
"Aaro",
""
],
[
"Ruzhansky",
"Michael",
""
]
] |
2311.11775 | Cristiano Costa | Cristiano Andr\'e da Costa, U\'elison Jean Lopes dos Santos, Eduardo
Souza dos Reis, Rodolfo Stoffel Antunes, Henrique Chaves Pacheco, Thayn\~a da
Silva Fran\c{c}a, Rodrigo da Rosa Righi, Jorge Luis Vict\'oria Barbosa,
Franklin Jebadoss, Jorge Montalvao, Rogerio Kunkel | Intelligent methods for business rule processing: State-of-the-art | 6 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this article, we provide an overview of the latest intelligent techniques
used for processing business rules. We have conducted a comprehensive survey of
the relevant literature on robot process automation, with a specific focus on
machine learning and other intelligent approaches. Additionally, we have
examined the top vendors in the market and their leading solutions to tackle
this issue.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 14:02:10 GMT"
}
] | 1,700,524,800,000 | [
[
"da Costa",
"Cristiano André",
""
],
[
"Santos",
"Uélison Jean Lopes dos",
""
],
[
"Reis",
"Eduardo Souza dos",
""
],
[
"Antunes",
"Rodolfo Stoffel",
""
],
[
"Pacheco",
"Henrique Chaves",
""
],
[
"França",
"Thaynã da Silva",
""
],
[
"Righi",
"Rodrigo da Rosa",
""
],
[
"Barbosa",
"Jorge Luis Victória",
""
],
[
"Jebadoss",
"Franklin",
""
],
[
"Montalvao",
"Jorge",
""
],
[
"Kunkel",
"Rogerio",
""
]
] |
2311.11802 | Eneko Osaba | Andoni Aranguren, Eneko Osaba, Silvia Urra-Uriarte and Patricia
Molina-Costa | Age-Friendly Route Planner: Calculating Comfortable Routes for Senior
Citizens | 11 pages, 5 figures, paper presented in the 11th World Conference on
Information Systems and Technologies (WorldCist'23) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The application of routing algorithms to real-world situations is a widely
studied research topic. Despite this, routing algorithms and applications are
usually developed for a general purpose, meaning that certain groups, such as
ageing people, are often marginalized due to the broad approach of the designed
algorithms. This situation may pose a problem in cities which are suffering a
slow but progressive ageing of their populations. With this motivation in mind,
this paper focuses on describing our implemented Age-Friendly Route Planner,
whose goal is to improve the experience in the city for senior citizens. In
order to measure the age-friendliness of a route, several variables have been
deemed, such as the number of amenities along the route, the amount of
comfortable elements found, or the avoidance of sloppy sections. In this paper,
we describe one of the main features of the Age-Friendly Route Planner: the
preference-based routes, and we also demonstrate how it can contribute to the
creation of adapted friendly routes.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 14:37:20 GMT"
}
] | 1,700,524,800,000 | [
[
"Aranguren",
"Andoni",
""
],
[
"Osaba",
"Eneko",
""
],
[
"Urra-Uriarte",
"Silvia",
""
],
[
"Molina-Costa",
"Patricia",
""
]
] |
2311.11811 | Giuseppe Pisano | Marco Billi, Alessandro Parenti, Giuseppe Pisano, Marco Sanchi | Large Language Models and Explainable Law: a Hybrid Methodology | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The paper advocates for LLMs to enhance the accessibility, usage and
explainability of rule-based legal systems, contributing to a democratic and
stakeholder-oriented view of legal technology. A methodology is developed to
explore the potential use of LLMs for translating the explanations produced by
rule-based systems, from high-level programming languages to natural language,
allowing all users a fast, clear, and accessible interaction with such
technologies. The study continues by building upon these explanations to
empower laypeople with the ability to execute complex juridical tasks on their
own, using a Chain of Prompts for the autonomous legal comparison of different
rule-based inferences, applied to the same factual case.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 14:47:20 GMT"
}
] | 1,700,524,800,000 | [
[
"Billi",
"Marco",
""
],
[
"Parenti",
"Alessandro",
""
],
[
"Pisano",
"Giuseppe",
""
],
[
"Sanchi",
"Marco",
""
]
] |
2311.11812 | Sumin Han | Sumin Han, Youngjun Park, Sonia Sabir, Jisun An, Dongman Lee | Improving Real Estate Appraisal with POI Integration and Areal Embedding | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Despite advancements in real estate appraisal methods, this study primarily
focuses on two pivotal challenges. Firstly, we explore the often-underestimated
impact of Points of Interest (POI) on property values, emphasizing the
necessity for a comprehensive, data-driven approach to feature selection.
Secondly, we integrate road-network-based Areal Embedding to enhance spatial
understanding for real estate appraisal. We first propose a revised method for
POI feature extraction, and discuss the impact of each POI for house price
appraisal. Then we present the Areal embedding-enabled Masked Multihead
Attention-based Spatial Interpolation for House Price Prediction (AMMASI)
model, an improvement upon the existing ASI model, which leverages masked
multi-head attention on geographic neighbor houses and similar-featured houses.
Our model outperforms current baselines and also offers promising avenues for
future optimization in real estate appraisal methodologies.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 14:48:09 GMT"
}
] | 1,700,524,800,000 | [
[
"Han",
"Sumin",
""
],
[
"Park",
"Youngjun",
""
],
[
"Sabir",
"Sonia",
""
],
[
"An",
"Jisun",
""
],
[
"Lee",
"Dongman",
""
]
] |
2311.11868 | Christopher Stone | Ian Miguel and Andr\'as Z. Salamon and Christopher Stone | Towards Exploratory Reformulation of Constraint Models | 13 pages, 6 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | It is well established that formulating an effective constraint model of a
problem of interest is crucial to the efficiency with which it can subsequently
be solved. Following from the observation that it is difficult, if not
impossible, to know a priori which of a set of candidate models will perform
best in practice, we envisage a system that explores the space of models
through a process of reformulation from an initial model, guided by performance
on a set of training instances from the problem class under consideration. We
plan to situate this system in a refinement-based approach, where a user writes
a constraint specification describing a problem above the level of abstraction
at which many modelling decisions are made. In this position paper we set out
our plan for an exploratory reformulation system, and discuss progress made so
far.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 16:04:56 GMT"
}
] | 1,700,524,800,000 | [
[
"Miguel",
"Ian",
""
],
[
"Salamon",
"András Z.",
""
],
[
"Stone",
"Christopher",
""
]
] |
2311.12010 | Stefaan Verhulst Dr | Stefaan G. Verhulst | Steering Responsible AI: A Case for Algorithmic Pluralism | 10 pages, working paper | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | In this paper, I examine questions surrounding AI neutrality through the
prism of existing literature and scholarship about mediation and media
pluralism. Such traditions, I argue, provide a valuable theoretical framework
for how we should approach the (likely) impending era of AI mediation. In
particular, I suggest examining further the notion of algorithmic pluralism.
Contrasting this notion to the dominant idea of algorithmic transparency, I
seek to describe what algorithmic pluralism may be, and present both its
opportunities and challenges. Implemented thoughtfully and responsibly, I
argue, Algorithmic or AI pluralism has the potential to sustain the diversity,
multiplicity, and inclusiveness that are so vital to democracy.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 18:45:04 GMT"
}
] | 1,700,524,800,000 | [
[
"Verhulst",
"Stefaan G.",
""
]
] |
2311.12047 | Jiali Cheng | Jiali Cheng, Hadi Amiri | Multimodal Machine Unlearning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Machine Unlearning is the process of removing specific training data samples
and their corresponding effects from an already trained model. It has
significant practical benefits, such as purging private, inaccurate, or
outdated information from trained models without the need for complete
re-training. Unlearning within a multimodal setting presents unique challenges
due to the intrinsic dependencies between different data modalities and the
expensive cost of training on large multimodal datasets and architectures.
Current approaches to machine unlearning have not fully addressed these
challenges. To bridge this gap, we introduce MMUL, a machine unlearning
approach specifically designed for multimodal data and models. MMUL formulates
the multimodal unlearning task by focusing on three key properties: (a):
modality decoupling, which effectively decouples the association between
individual unimodal data points within multimodal inputs marked for deletion,
rendering them as unrelated data points within the model's context, (b):
unimodal knowledge retention, which retains the unimodal representation
capability of the model post-unlearning, and (c): multimodal knowledge
retention, which retains the multimodal representation capability of the model
post-unlearning. MMUL is efficient to train and is not constrained by the
requirement of using a strongly convex loss. Experiments on two multimodal
models and four multimodal benchmark datasets, including vision-language and
graph-language datasets, show that MMUL outperforms existing baselines, gaining
an average improvement of +17.6 points against the best-performing unimodal
baseline in distinguishing between deleted and remaining data. In addition,
MMUL can largely maintain pre-existing knowledge of the original model post
unlearning, with a performance gap of only 0.3 points compared to retraining a
new model from scratch.
| [
{
"version": "v1",
"created": "Sat, 18 Nov 2023 08:30:38 GMT"
}
] | 1,700,611,200,000 | [
[
"Cheng",
"Jiali",
""
],
[
"Amiri",
"Hadi",
""
]
] |
2311.12207 | Beishui Liao | Beishui Liao and Leendert van der Torre | Defense semantics of argumentation: revisit | arXiv admin note: text overlap with arXiv:1705.00303 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this paper we introduce a novel semantics, called defense semantics, for
Dung's abstract argumentation frameworks in terms of a notion of (partial)
defence, which is a triple encoding that one argument is (partially) defended
by another argument via attacking the attacker of the first argument. In terms
of defense semantics, we show that defenses related to self-attacked arguments
and arguments in 3-cycles are unsatifiable under any situation and therefore
can be removed without affecting the defense semantics of an AF. Then, we
introduce a new notion of defense equivalence of AFs, and compare defense
equivalence with standard equivalence and strong equivalence, respectively.
Finally, by exploiting defense semantics, we define two kinds of reasons for
accepting arguments, i.e., direct reasons and root reasons, and a notion of
root equivalence of AFs that can be used in argumentation summarization.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 21:51:45 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Nov 2023 14:07:51 GMT"
}
] | 1,700,697,600,000 | [
[
"Liao",
"Beishui",
""
],
[
"van der Torre",
"Leendert",
""
]
] |
2311.12229 | Phillip Howard | Shachar Rosenman, Vasudev Lal, and Phillip Howard | NeuroPrompts: An Adaptive Framework to Optimize Prompts for
Text-to-Image Generation | Accepted to EACL 2024 System Demonstration Track | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Despite impressive recent advances in text-to-image diffusion models,
obtaining high-quality images often requires prompt engineering by humans who
have developed expertise in using them. In this work, we present NeuroPrompts,
an adaptive framework that automatically enhances a user's prompt to improve
the quality of generations produced by text-to-image models. Our framework
utilizes constrained text decoding with a pre-trained language model that has
been adapted to generate prompts similar to those produced by human prompt
engineers. This approach enables higher-quality text-to-image generations and
provides user control over stylistic features via constraint set specification.
We demonstrate the utility of our framework by creating an interactive
application for prompt enhancement and image generation using Stable Diffusion.
Additionally, we conduct experiments utilizing a large dataset of
human-engineered prompts for text-to-image generation and show that our
approach automatically produces enhanced prompts that result in superior image
quality. We make our code and a screencast video demo of NeuroPrompts publicly
available.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 22:57:47 GMT"
},
{
"version": "v2",
"created": "Sat, 6 Apr 2024 00:17:01 GMT"
}
] | 1,712,620,800,000 | [
[
"Rosenman",
"Shachar",
""
],
[
"Lal",
"Vasudev",
""
],
[
"Howard",
"Phillip",
""
]
] |
2311.12241 | Saketh Reddy Karra | Saketh Reddy Karra, Theja Tulabandhula | InteraSSort: Interactive Assortment Planning Using Large Language Models | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Assortment planning, integral to multiple commercial offerings, is a key
problem studied in e-commerce and retail settings. Numerous variants of the
problem along with their integration into business solutions have been
thoroughly investigated in the existing literature. However, the nuanced
complexities of in-store planning and a lack of optimization proficiency among
store planners with strong domain expertise remain largely overlooked. These
challenges frequently necessitate collaborative efforts with multiple
stakeholders which often lead to prolonged decision-making processes and
significant delays. To mitigate these challenges and capitalize on the
advancements of Large Language Models (LLMs), we propose an interactive
assortment planning framework, InteraSSort that augments LLMs with optimization
tools to assist store planners in making decisions through interactive
conversations. Specifically, we develop a solution featuring a user-friendly
interface that enables users to express their optimization objectives as input
text prompts to InteraSSort and receive tailored optimized solutions as output.
Our framework extends beyond basic functionality by enabling the inclusion of
additional constraints through interactive conversation, facilitating precise
and highly customized decision-making. Extensive experiments demonstrate the
effectiveness of our framework and potential extensions to a broad range of
operations management challenges.
| [
{
"version": "v1",
"created": "Mon, 20 Nov 2023 23:36:41 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Jan 2024 17:53:30 GMT"
}
] | 1,704,844,800,000 | [
[
"Karra",
"Saketh Reddy",
""
],
[
"Tulabandhula",
"Theja",
""
]
] |
2311.12307 | Ning Xu | Ning Xu, Yifei Gao, Hongshuo Tian, Yongdong Zhang, An-An Liu | Causality is all you need | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the fundamental statistics course, students are taught to remember the
well-known saying: "Correlation is not Causation". Till now, statistics (i.e.,
correlation) have developed various successful frameworks, such as Transformer
and Pre-training large-scale models, which have stacked multiple parallel
self-attention blocks to imitate a wide range of tasks. However, in the
causation community, how to build an integrated causal framework still remains
an untouched domain despite its excellent intervention capabilities. In this
paper, we propose the Causal Graph Routing (CGR) framework, an integrated
causal scheme relying entirely on the intervention mechanisms to reveal the
cause-effect forces hidden in data. Specifically, CGR is composed of a stack of
causal layers. Each layer includes a set of parallel deconfounding blocks from
different causal graphs. We combine these blocks via the concept of the
proposed sufficient cause, which allows the model to dynamically select the
suitable deconfounding methods in each layer. CGR is implemented as the stacked
networks, integrating no confounder, back-door adjustment, front-door
adjustment, and probability of sufficient cause. We evaluate this framework on
two classical tasks of CV and NLP. Experiments show CGR can surpass the current
state-of-the-art methods on both Visual Question Answer and Long Document
Classification tasks. In particular, CGR has great potential in building the
"causal" pre-training large-scale model that effectively generalizes to diverse
tasks. It will improve the machines' comprehension of causal relationships
within a broader semantic space.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 02:53:40 GMT"
}
] | 1,700,611,200,000 | [
[
"Xu",
"Ning",
""
],
[
"Gao",
"Yifei",
""
],
[
"Tian",
"Hongshuo",
""
],
[
"Zhang",
"Yongdong",
""
],
[
"Liu",
"An-An",
""
]
] |
2311.12320 | Can Cui | Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang,
Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, Tianren Gao, Erlong Li,
Kun Tang, Zhipeng Cao, Tong Zhou, Ao Liu, Xinrui Yan, Shuqi Mei, Jianguo Cao,
Ziran Wang, Chao Zheng | A Survey on Multimodal Large Language Models for Autonomous Driving | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | With the emergence of Large Language Models (LLMs) and Vision Foundation
Models (VFMs), multimodal AI systems benefiting from large models have the
potential to equally perceive the real world, make decisions, and control tools
as humans. In recent months, LLMs have shown widespread attention in autonomous
driving and map systems. Despite its immense potential, there is still a lack
of a comprehensive understanding of key challenges, opportunities, and future
endeavors to apply in LLM driving systems. In this paper, we present a
systematic investigation in this field. We first introduce the background of
Multimodal Large Language Models (MLLMs), the multimodal models development
using LLMs, and the history of autonomous driving. Then, we overview existing
MLLM tools for driving, transportation, and map systems together with existing
datasets and benchmarks. Moreover, we summarized the works in The 1st WACV
Workshop on Large Language and Vision Models for Autonomous Driving (LLVM-AD),
which is the first workshop of its kind regarding LLMs in autonomous driving.
To further promote the development of this field, we also discuss several
important problems regarding using MLLMs in autonomous driving systems that
need to be solved by both academia and industry.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 03:32:01 GMT"
}
] | 1,700,611,200,000 | [
[
"Cui",
"Can",
""
],
[
"Ma",
"Yunsheng",
""
],
[
"Cao",
"Xu",
""
],
[
"Ye",
"Wenqian",
""
],
[
"Zhou",
"Yang",
""
],
[
"Liang",
"Kaizhao",
""
],
[
"Chen",
"Jintai",
""
],
[
"Lu",
"Juanwu",
""
],
[
"Yang",
"Zichong",
""
],
[
"Liao",
"Kuei-Da",
""
],
[
"Gao",
"Tianren",
""
],
[
"Li",
"Erlong",
""
],
[
"Tang",
"Kun",
""
],
[
"Cao",
"Zhipeng",
""
],
[
"Zhou",
"Tong",
""
],
[
"Liu",
"Ao",
""
],
[
"Yan",
"Xinrui",
""
],
[
"Mei",
"Shuqi",
""
],
[
"Cao",
"Jianguo",
""
],
[
"Wang",
"Ziran",
""
],
[
"Zheng",
"Chao",
""
]
] |
2311.12431 | Barbara Tillmann | Daniel Defays, Robert French (LEAD), Barbara Tillmann (LEAD) | A recurrent connectionist model of melody perception : An exploration
using TRACX2 | null | Cognitive Science, 2023, 47 (4) | 10.1111/cogs.13283 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Are similar, or even identical, mechanisms used in the computational modeling
of speech segmentation, serial image processing and music processing? We
address this question by exploring how TRACX2, (French et al., 2011; French \&
Cottrell, 2014; Mareschal \& French, 2017), a recognition-based, recursive
connectionist autoencoder model of chunking and sequence segmentation, which
has successfully simulated speech and serial-image processing, might be applied
to elementary melody perception. The model, a three-layer autoencoder that
recognizes ''chunks'' of short sequences of intervals that have been frequently
encountered on input, is trained on the tone intervals of melodically simple
French children's songs. It dynamically incorporates the internal
representations of these chunks into new input. Its internal representations
cluster in a manner that is consistent with ''human-recognizable'' melodic
categories. TRACX2 is sensitive to both contour and proximity information in
the musical chunks that it encounters in its input. It shows the
''end-of-word'' superiority effect demonstrated by Saffran et al. (1999) for
short musical phrases. The overall findings suggest that the recursive
autoassociative chunking mechanism, as implemented in TRACX2, may be a general
segmentation and chunking mechanism, underlying not only word-and
imagechunking, but also elementary melody processing.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 08:43:06 GMT"
}
] | 1,700,611,200,000 | [
[
"Defays",
"Daniel",
"",
"LEAD"
],
[
"French",
"Robert",
"",
"LEAD"
],
[
"Tillmann",
"Barbara",
"",
"LEAD"
]
] |
2311.12447 | Miriam Rateike | Miriam Rateike, Isabel Valera and Patrick Forr\'e | Designing Long-term Group Fair Policies in Dynamical Systems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Neglecting the effect that decisions have on individuals (and thus, on the
underlying data distribution) when designing algorithmic decision-making
policies may increase inequalities and unfairness in the long term - even if
fairness considerations were taken in the policy design process. In this paper,
we propose a novel framework for achieving long-term group fairness in
dynamical systems, in which current decisions may affect an individual's
features in the next step, and thus, future decisions. Specifically, our
framework allows us to identify a time-independent policy that converges, if
deployed, to the targeted fair stationary state of the system in the long term,
independently of the initial data distribution. We model the system dynamics
with a time-homogeneous Markov chain and optimize the policy leveraging the
Markov chain convergence theorem to ensure unique convergence. We provide
examples of different targeted fair states of the system, encompassing a range
of long-term goals for society and policymakers. Furthermore, we show how our
approach facilitates the evaluation of different long-term targets by examining
their impact on the group-conditional population distribution in the long term
and how it evolves until convergence.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 08:58:50 GMT"
}
] | 1,700,611,200,000 | [
[
"Rateike",
"Miriam",
""
],
[
"Valera",
"Isabel",
""
],
[
"Forré",
"Patrick",
""
]
] |
2311.12448 | Shufan JIANG | Shufan Jiang (VALDA), Pierre Senellart (DI-ENS, VALDA) | Extracting Definienda in Mathematical Scholarly Articles with
Transformers | In the Proceedings of the 2nd Workshop on Information Extraction from
Scientific Publications (WIESP 2023) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider automatically identifying the defined term within a mathematical
definition from the text of an academic article. Inspired by the development of
transformer-based natural language processing applications, we pose the problem
as (a) a token-level classification task using fine-tuned pre-trained
transformers; and (b) a question-answering task using a generalist large
language model (GPT). We also propose a rule-based approach to build a labeled
dataset from the LATEX source of papers. Experimental results show that it is
possible to reach high levels of precision and recall using either recent (and
expensive) GPT 4 or simpler pre-trained models fine-tuned on our task.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 08:58:57 GMT"
}
] | 1,700,611,200,000 | [
[
"Jiang",
"Shufan",
"",
"VALDA"
],
[
"Senellart",
"Pierre",
"",
"DI-ENS, VALDA"
]
] |
2311.12465 | Mayukh Bagchi | Mattia Fumagalli, Marco Boffo, Daqian Shi, Mayukh Bagchi and Fausto
Giunchiglia | Towards a Gateway for Knowledge Graph Schemas Collection, Analysis, and
Embedding | Ontology Showcase and Demonstrations Track, 9th Joint Ontology
Workshops (JOWO 2023), Co-located with FOIS 2023, 19-20 July, 2023,
Sherbrooke, Qu\'ebec, Canada. arXiv admin note: substantial text overlap with
arXiv:2207.06112 | null | null | DISIKNOWDIVE21112023 | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | One of the significant barriers to the training of statistical models on
knowledge graphs is the difficulty that scientists have in finding the best
input data to address their prediction goal. In addition to this, a key
challenge is to determine how to manipulate these relational data, which are
often in the form of particular triples (i.e., subject, predicate, object), to
enable the learning process. Currently, many high-quality catalogs of knowledge
graphs, are available. However, their primary goal is the re-usability of these
resources, and their interconnection, in the context of the Semantic Web. This
paper describes the LiveSchema initiative, namely, a first version of a gateway
that has the main scope of leveraging the gold mine of data collected by many
existing catalogs collecting relational data like ontologies and knowledge
graphs. At the current state, LiveSchema contains - 1000 datasets from 4 main
sources and offers some key facilities, which allow to: i) evolving LiveSchema,
by aggregating other source catalogs and repositories as input sources; ii)
querying all the collected resources; iii) transforming each given dataset into
formal concept analysis matrices that enable analysis and visualization
services; iv) generating models and tensors from each given dataset.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 09:22:02 GMT"
}
] | 1,700,611,200,000 | [
[
"Fumagalli",
"Mattia",
""
],
[
"Boffo",
"Marco",
""
],
[
"Shi",
"Daqian",
""
],
[
"Bagchi",
"Mayukh",
""
],
[
"Giunchiglia",
"Fausto",
""
]
] |
2311.12472 | Zhang Wentao | Jiahao Ji, Wentao Zhang, Jingyuan Wang, Yue He and Chao Huang | Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and
Modeling | 14 pages, 9 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As an important application of spatio-temporal (ST) data, ST traffic
forecasting plays a crucial role in improving urban travel efficiency and
promoting sustainable development. In practice, the dynamics of traffic data
frequently undergo distributional shifts attributed to external factors such as
time evolution and spatial differences. This entails forecasting models to
handle the out-of-distribution (OOD) issue where test data is distributed
differently from training data. In this work, we first formalize the problem by
constructing a causal graph of past traffic data, future traffic data, and
external ST contexts. We reveal that the failure of prior arts in OOD traffic
data is due to ST contexts acting as a confounder, i.e., the common cause for
past data and future ones. Then, we propose a theoretical solution named
Disentangled Contextual Adjustment (DCA) from a causal lens. It differentiates
invariant causal correlations against variant spurious ones and deconfounds the
effect of ST contexts. On top of that, we devise a Spatio-Temporal
sElf-superVised dEconfounding (STEVE) framework. It first encodes traffic data
into two disentangled representations for associating invariant and variant ST
contexts. Then, we use representative ST contexts from three conceptually
different perspectives (i.e., temporal, spatial, and semantic) as
self-supervised signals to inject context information into both
representations. In this way, we improve the generalization ability of the
learned context-oriented representations to OOD ST traffic forecasting.
Comprehensive experiments on four large-scale benchmark datasets demonstrate
that our STEVE consistently outperforms the state-of-the-art baselines across
various ST OOD scenarios.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 09:33:13 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Mar 2024 12:57:27 GMT"
}
] | 1,709,769,600,000 | [
[
"Ji",
"Jiahao",
""
],
[
"Zhang",
"Wentao",
""
],
[
"Wang",
"Jingyuan",
""
],
[
"He",
"Yue",
""
],
[
"Huang",
"Chao",
""
]
] |
2311.12521 | Keshav Ramani | Keshav Ramani, Daniel Borrajo | Classification of Tabular Data by Text Processing | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural Language Processing technology has advanced vastly in the past
decade. Text processing has been successfully applied to a wide variety of
domains. In this paper, we propose a novel framework, Text Based
Classification(TBC), that uses state of the art text processing techniques to
solve classification tasks on tabular data. We provide a set of controlled
experiments where we present the benefits of using this approach against other
classification methods. Experimental results on several data sets also show
that this framework achieves comparable performance to that of several state of
the art models in accuracy, precision and recall of predicted classes.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 10:56:07 GMT"
}
] | 1,700,611,200,000 | [
[
"Ramani",
"Keshav",
""
],
[
"Borrajo",
"Daniel",
""
]
] |
2311.12548 | Xiaoli Tang | Xiaoli Tang, Han Yu | Multi-Session Budget Optimization for Forward Auction-based Federated
Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Auction-based Federated Learning (AFL) has emerged as an important research
field in recent years. The prevailing strategies for FL model users (MUs)
assume that the entire team of the required data owners (DOs) for an FL task
must be assembled before training can commence. In practice, an MU can trigger
the FL training process multiple times. DOs can thus be gradually recruited
over multiple FL model training sessions. Existing bidding strategies for AFL
MUs are not designed to handle such scenarios. Therefore, the problem of
multi-session AFL remains open. To address this problem, we propose the
Multi-session Budget Optimization Strategy for forward Auction-based Federated
Learning (MultiBOS-AFL). Based on hierarchical reinforcement learning,
MultiBOS-AFL jointly optimizes inter-session budget pacing and intra-session
bidding for AFL MUs, with the objective of maximizing the total utility.
Extensive experiments on six benchmark datasets show that it significantly
outperforms seven state-of-the-art approaches. On average, MultiBOS-AFL
achieves 12.28% higher utility, 14.52% more data acquired through auctions for
a given budget, and 1.23% higher test accuracy achieved by the resulting FL
model compared to the best baseline. To the best of our knowledge, it is the
first budget optimization decision support method with budget pacing capability
designed for MUs in multi-session forward auction-based federated learning
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 11:57:41 GMT"
}
] | 1,700,611,200,000 | [
[
"Tang",
"Xiaoli",
""
],
[
"Yu",
"Han",
""
]
] |
2311.12604 | Caesar Wu | Caesar Wu, Yuan-Fang Li, Jian Li, Jingjing Xu, Bouvry Pascal | Trustworthy AI: Deciding What to Decide | 20 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | When engaging in strategic decision-making, we are frequently confronted with
overwhelming information and data. The situation can be further complicated
when certain pieces of evidence contradict each other or become paradoxical.
The primary challenge is how to determine which information can be trusted when
we adopt Artificial Intelligence (AI) systems for decision-making. This issue
is known as deciding what to decide or Trustworthy AI. However, the AI system
itself is often considered an opaque black box. We propose a new approach to
address this issue by introducing a novel framework of Trustworthy AI (TAI)
encompassing three crucial components of AI: representation space, loss
function, and optimizer. Each component is loosely coupled with four TAI
properties. Altogether, the framework consists of twelve TAI properties. We aim
to use this framework to conduct the TAI experiments by quantitive and
qualitative research methods to satisfy TAI properties for the decision-making
context. The framework allows us to formulate an optimal prediction model
trained by the given dataset for applying the strategic investment decision of
credit default swaps (CDS) in the technology sector. Finally, we provide our
view of the future direction of TAI research
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 13:43:58 GMT"
}
] | 1,700,611,200,000 | [
[
"Wu",
"Caesar",
""
],
[
"Li",
"Yuan-Fang",
""
],
[
"Li",
"Jian",
""
],
[
"Xu",
"Jingjing",
""
],
[
"Pascal",
"Bouvry",
""
]
] |
2311.12719 | Pranav Devaraj | Pranav Nataraj Devaraj, Rakesh Teja P V, Aaryav Gangrade, Manoj Kumar
R | Development of a Legal Document AI-Chatbot | 5 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the exponential growth of digital data and the increasing complexity of
legal documentation, there is a pressing need for efficient and intelligent
tools to streamline the handling of legal documents.With the recent
developments in the AI field, especially in chatbots, it cannot be ignored as a
very compelling solution to this problem.An insight into the process of
creating a Legal Documentation AI Chatbot with as many relevant features as
possible within the given time frame is presented.The development of each
component of the chatbot is presented in detail.Each component's workings and
functionality has been discussed.Starting from the build of the Android app and
the Langchain query processing code till the integration of both through a
Flask backend and REST API methods.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 16:48:10 GMT"
}
] | 1,700,611,200,000 | [
[
"Devaraj",
"Pranav Nataraj",
""
],
[
"P",
"Rakesh Teja",
"V"
],
[
"Gangrade",
"Aaryav",
""
],
[
"R",
"Manoj Kumar",
""
]
] |
2311.12755 | Carine Rebello | Carine Menezes Rebello, Johannes J\"aschkea, and Idelfonso B. R.
Nogueira | Digital Twin Framework for Optimal and Autonomous Decision-Making in
Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and
Gas Industry | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The concept of creating a virtual copy of a complete Cyber-Physical System
opens up numerous possibilities, including real-time assessments of the
physical environment and continuous learning from the system to provide
reliable and precise information. This process, known as the twinning process
or the development of a digital twin (DT), has been widely adopted across
various industries. However, challenges arise when considering the
computational demands of implementing AI models, such as those employed in
digital twins, in real-time information exchange scenarios. This work proposes
a digital twin framework for optimal and autonomous decision-making applied to
a gas-lift process in the oil and gas industry, focusing on enhancing the
robustness and adaptability of the DT. The framework combines Bayesian
inference, Monte Carlo simulations, transfer learning, online learning, and
novel strategies to confer cognition to the DT, including model
hyperdimensional reduction and cognitive tack. Consequently, creating a
framework for efficient, reliable, and trustworthy DT identification was
possible. The proposed approach addresses the current gap in the literature
regarding integrating various learning techniques and uncertainty management in
digital twin strategies. This digital twin framework aims to provide a reliable
and efficient system capable of adapting to changing environments and
incorporating prediction uncertainty, thus enhancing the overall
decision-making process in complex, real-world scenarios. Additionally, this
work lays the foundation for further developments in digital twins for process
systems engineering, potentially fostering new advancements and applications
across various industrial sectors.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 18:02:52 GMT"
}
] | 1,700,611,200,000 | [
[
"Rebello",
"Carine Menezes",
""
],
[
"Jäschkea",
"Johannes",
""
],
[
"Nogueira",
"Idelfonso B. R.",
""
]
] |
2311.12990 | Gyeong-Geon Lee Dr | Gyeong-Geon Lee, and Xiaoming Zhai | NERIF: GPT-4V for Automatic Scoring of Drawn Models | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Scoring student-drawn models is time-consuming. Recently released GPT-4V
provides a unique opportunity to advance scientific modeling practices by
leveraging the powerful image processing capability. To test this ability
specifically for automatic scoring, we developed a method NERIF
(Notation-Enhanced Rubric Instruction for Few-shot Learning) employing
instructional note and rubrics to prompt GPT-4V to score students' drawn models
for science phenomena. We randomly selected a set of balanced data (N = 900)
that includes student-drawn models for six modeling assessment tasks. Each
model received a score from GPT-4V ranging at three levels: 'Beginning,'
'Developing,' or 'Proficient' according to scoring rubrics. GPT-4V scores were
compared with human experts' scores to calculate scoring accuracy. Results show
that GPT-4V's average scoring accuracy was mean =.51, SD = .037. Specifically,
average scoring accuracy was .64 for the 'Beginning' class, .62 for the
'Developing' class, and .26 for the 'Proficient' class, indicating that more
proficient models are more challenging to score. Further qualitative study
reveals how GPT-4V retrieves information from image input, including problem
context, example evaluations provided by human coders, and students' drawing
models. We also uncovered how GPT-4V catches the characteristics of
student-drawn models and narrates them in natural language. At last, we
demonstrated how GPT-4V assigns scores to student-drawn models according to the
given scoring rubric and instructional notes. Our findings suggest that the
NERIF is an effective approach for employing GPT-4V to score drawn models. Even
though there is space for GPT-4V to improve scoring accuracy, some mis-assigned
scores seemed interpretable to experts. The results of this study show that
utilizing GPT-4V for automatic scoring of student-drawn models is promising.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 20:52:04 GMT"
},
{
"version": "v2",
"created": "Sun, 24 Dec 2023 04:23:29 GMT"
}
] | 1,703,808,000,000 | [
[
"Lee",
"Gyeong-Geon",
""
],
[
"Zhai",
"Xiaoming",
""
]
] |
2311.13063 | Zachary Englhardt | Zachary Englhardt, Chengqian Ma, Margaret E. Morris, Xuhai "Orson" Xu,
Chun-Cheng Chang, Lianhui Qin, Daniel McDuff, Xin Liu, Shwetak Patel, Vikram
Iyer | From Classification to Clinical Insights: Towards Analyzing and
Reasoning About Mobile and Behavioral Health Data With Large Language Models | null | null | 10.1145/3659604 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Passively collected behavioral health data from ubiquitous sensors holds
significant promise to provide mental health professionals insights from
patient's daily lives; however, developing analysis tools to use this data in
clinical practice requires addressing challenges of generalization across
devices and weak or ambiguous correlations between the measured signals and an
individual's mental health. To address these challenges, we take a novel
approach that leverages large language models (LLMs) to synthesize clinically
useful insights from multi-sensor data. We develop chain of thought prompting
methods that use LLMs to generate reasoning about how trends in data such as
step count and sleep relate to conditions like depression and anxiety. We first
demonstrate binary depression classification with LLMs achieving accuracies of
61.1% which exceed the state of the art. While it is not robust for clinical
use, this leads us to our key finding: even more impactful and valued than
classification is a new human-AI collaboration approach in which clinician
experts interactively query these tools and combine their domain expertise and
context about the patient with AI generated reasoning to support clinical
decision-making. We find models like GPT-4 correctly reference numerical data
75% of the time, and clinician participants express strong interest in using
this approach to interpret self-tracking data.
| [
{
"version": "v1",
"created": "Tue, 21 Nov 2023 23:53:27 GMT"
},
{
"version": "v2",
"created": "Sat, 25 Nov 2023 19:45:43 GMT"
}
] | 1,717,718,400,000 | [
[
"Englhardt",
"Zachary",
""
],
[
"Ma",
"Chengqian",
""
],
[
"Morris",
"Margaret E.",
""
],
[
"Xu",
"Xuhai \"Orson\"",
""
],
[
"Chang",
"Chun-Cheng",
""
],
[
"Qin",
"Lianhui",
""
],
[
"McDuff",
"Daniel",
""
],
[
"Liu",
"Xin",
""
],
[
"Patel",
"Shwetak",
""
],
[
"Iyer",
"Vikram",
""
]
] |
2311.13160 | Wensheng Gan | Wensheng Gan, Zhenlian Qi, Jiayang Wu, Jerry Chun-Wei Lin | Large Language Models in Education: Vision and Opportunities | IEEE BigData 2023. 10 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid development of artificial intelligence technology, large
language models (LLMs) have become a hot research topic. Education plays an
important role in human social development and progress. Traditional education
faces challenges such as individual student differences, insufficient
allocation of teaching resources, and assessment of teaching effectiveness.
Therefore, the applications of LLMs in the field of digital/smart education
have broad prospects. The research on educational large models (EduLLMs) is
constantly evolving, providing new methods and approaches to achieve
personalized learning, intelligent tutoring, and educational assessment goals,
thereby improving the quality of education and the learning experience. This
article aims to investigate and summarize the application of LLMs in smart
education. It first introduces the research background and motivation of LLMs
and explains the essence of LLMs. It then discusses the relationship between
digital education and EduLLMs and summarizes the current research status of
educational large models. The main contributions are the systematic summary and
vision of the research background, motivation, and application of large models
for education (LLM4Edu). By reviewing existing research, this article provides
guidance and insights for educators, researchers, and policy-makers to gain a
deep understanding of the potential and challenges of LLM4Edu. It further
provides guidance for further advancing the development and application of
LLM4Edu, while still facing technical, ethical, and practical challenges
requiring further research and exploration.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 05:04:20 GMT"
}
] | 1,700,697,600,000 | [
[
"Gan",
"Wensheng",
""
],
[
"Qi",
"Zhenlian",
""
],
[
"Wu",
"Jiayang",
""
],
[
"Lin",
"Jerry Chun-Wei",
""
]
] |
2311.13165 | Wensheng Gan | Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, Philip S. Yu | Multimodal Large Language Models: A Survey | IEEE BigData 2023. 10 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The exploration of multimodal language models integrates multiple data types,
such as images, text, language, audio, and other heterogeneity. While the
latest large language models excel in text-based tasks, they often struggle to
understand and process other data types. Multimodal models address this
limitation by combining various modalities, enabling a more comprehensive
understanding of diverse data. This paper begins by defining the concept of
multimodal and examining the historical development of multimodal algorithms.
Furthermore, we introduce a range of multimodal products, focusing on the
efforts of major technology companies. A practical guide is provided, offering
insights into the technical aspects of multimodal models. Moreover, we present
a compilation of the latest algorithms and commonly used datasets, providing
researchers with valuable resources for experimentation and evaluation. Lastly,
we explore the applications of multimodal models and discuss the challenges
associated with their development. By addressing these aspects, this paper aims
to facilitate a deeper understanding of multimodal models and their potential
in various domains.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 05:15:12 GMT"
}
] | 1,700,697,600,000 | [
[
"Wu",
"Jiayang",
""
],
[
"Gan",
"Wensheng",
""
],
[
"Chen",
"Zefeng",
""
],
[
"Wan",
"Shicheng",
""
],
[
"Yu",
"Philip S.",
""
]
] |
2311.13206 | Mosab Farea | Mosab S. M. Farea, zhe chen | Breast Cancer classification by adaptive weighted average ensemble of
previously trained models | 12 pages article | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Breast cancer is a serious disease that inflicts millions of people each
year, and the number of cases is increasing. Early detection is the best way to
reduce the impact of the disease. Researchers have developed many techniques to
detect breast cancer, including the use of histopathology images in CAD
systems. This research proposes a technique that combine already fully trained
model using adaptive average ensemble, this is different from the literature
which uses average ensemble before training and the average ensemble is trained
simultaneously. Our approach is different because it used adaptive average
ensemble after training which has increased the performance of evaluation
metrics. It averages the outputs of every trained model, and every model will
have weight according to its accuracy. The accuracy in the adaptive weighted
ensemble model has achieved 98% where the accuracy has increased by 1 percent
which is better than the best participating model in the ensemble which was
97%. Also, it decreased the numbers of false positive and false negative and
enhanced the performance metrics.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 07:33:43 GMT"
}
] | 1,700,697,600,000 | [
[
"Farea",
"Mosab S. M.",
""
],
[
"chen",
"zhe",
""
]
] |
2311.13213 | Robert Kudeli\'c PhD | Robert Kudeli\'c and Tamara \v{S}maguc and Sherry Robinson | Artificial Intelligence in the Service of Entrepreneurial Finance:
Knowledge Structure and the Foundational Algorithmic Paradigm | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | While the application of Artificial Intelligence in Finance has a long
tradition, its potential in Entrepreneurship has been intensively explored only
recently. In this context, Entrepreneurial Finance is a particularly fertile
ground for future Artificial Intelligence proliferation. To support the latter,
the study provides a bibliometric review of Artificial Intelligence
applications in (1) entrepreneurial finance literature, and (2) corporate
finance literature with implications for Entrepreneurship. Rigorous search and
screening procedures of the scientific database Web of Science Core Collection
resulted in the identification of 1890 relevant journal articles subjected to
analysis. The bibliometric analysis gives a rich insight into the knowledge
field's conceptual, intellectual, and social structure, indicating nascent and
underdeveloped research directions. As far as we were able to identify, this is
the first study to map and bibliometrically analyze the academic field
concerning the relationship between Artificial Intelligence, Entrepreneurship,
and Finance, and the first review that deals with Artificial Intelligence
methods in Entrepreneurship. According to the results, Artificial Neural
Network, Deep Neural Network and Support Vector Machine are highly represented
in almost all identified topic niches. At the same time, applying Topic
Modeling, Fuzzy Neural Network and Growing Hierarchical Self-organizing Map is
quite rare. As an element of the research, and before final remarks, the
article deals as well with a discussion of certain gaps in the relationship
between Computer Science and Economics. These gaps do represent problems in the
application of Artificial Intelligence in Economic Science. As a way to at
least in part remedy this situation, the foundational paradigm and the bespoke
demonstration of the Monte Carlo randomized algorithm are presented.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 07:58:46 GMT"
}
] | 1,700,697,600,000 | [
[
"Kudelić",
"Robert",
""
],
[
"Šmaguc",
"Tamara",
""
],
[
"Robinson",
"Sherry",
""
]
] |
2311.13262 | Saad Shaikh | Saad Shaikh, Rajat bendre, Sakshi Mhaske | The Rise of Creative Machines: Exploring the Impact of Generative AI | The impact of generative AI on research, product creation, ethical
concerns etc is examined in this six-page article. Figures 1, 2, and 3, which
are essential to the analysis, are included in the discussion along with
opportunities, hazards, and ethical considerations | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This study looks at how generative artificial intelligence (AI) can
revolutionize marketing, product development, and research. It discusses the
latest developments in the field, easy-to-use resources, and moral and social
hazards. In addition to addressing mitigating techniques for issues like
prejudice and disinformation, the debate emphasizes the significance of
responsible development through continual stakeholder communication and ethical
principles.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 09:27:08 GMT"
}
] | 1,700,697,600,000 | [
[
"Shaikh",
"Saad",
""
],
[
"bendre",
"Rajat",
""
],
[
"Mhaske",
"Sakshi",
""
]
] |
2311.13286 | Michael Klenk | Michael Klenk | Algorithmic Transparency and Manipulation | null | null | 10.1007/s13347-023-00678-9 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A series of recent papers raises worries about the manipulative potential of
algorithmic transparency. But while the concern is apt and relevant, it is
based on a fraught understanding of manipulation. Therefore, this paper draws
attention to the indifference view of manipulation, which explains better than
the vulnerability view why algorithmic transparency has manipulative potential.
The paper also raises pertinent research questions for future studies of
manipulation in the context of algorithmic transparency.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 10:09:06 GMT"
}
] | 1,700,697,600,000 | [
[
"Klenk",
"Michael",
""
]
] |
2311.13373 | Bin Liu | Zihao Zhou, Bin Hu, Chenyang Zhao, Pu Zhang, Bin Liu | Large Language Model as a Policy Teacher for Training Reinforcement
Learning Agents | Accepted and Published by IJCAI 2024 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recent studies have uncovered the potential of Large Language Models (LLMs)
in addressing complex sequential decision-making tasks through the provision of
high-level instructions. However, LLM-based agents lack specialization in
tackling specific target problems, particularly in real-time dynamic
environments. Additionally, deploying an LLM-based agent in practical scenarios
can be both costly and time-consuming. On the other hand, reinforcement
learning (RL) approaches train agents that specialize in the target task but
often suffer from low sampling efficiency and high exploration costs. In this
paper, we introduce a novel framework that addresses these challenges by
training a smaller, specialized student RL agent using instructions from an
LLM-based teacher agent. By incorporating the guidance from the teacher agent,
the student agent can distill the prior knowledge of the LLM into its own
model. Consequently, the student agent can be trained with significantly less
data. Moreover, through further training with environment feedback, the student
agent surpasses the capabilities of its teacher for completing the target task.
We conducted experiments on challenging MiniGrid and Habitat environments,
specifically designed for embodied AI research, to evaluate the effectiveness
of our framework. The results clearly demonstrate that our approach achieves
superior performance compared to strong baseline methods. Our code is available
at https://github.com/ZJLAB-AMMI/LLM4Teach.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 13:15:42 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Nov 2023 09:24:25 GMT"
},
{
"version": "v3",
"created": "Wed, 29 Nov 2023 08:39:37 GMT"
},
{
"version": "v4",
"created": "Mon, 22 Jan 2024 11:08:04 GMT"
},
{
"version": "v5",
"created": "Mon, 22 Apr 2024 15:17:58 GMT"
},
{
"version": "v6",
"created": "Mon, 27 May 2024 14:00:23 GMT"
}
] | 1,716,854,400,000 | [
[
"Zhou",
"Zihao",
""
],
[
"Hu",
"Bin",
""
],
[
"Zhao",
"Chenyang",
""
],
[
"Zhang",
"Pu",
""
],
[
"Liu",
"Bin",
""
]
] |
2311.13379 | Sieben Bocklandt | Sieben Bocklandt, Vincent Derkinderen, Koen Vanderstraeten, Wouter
Pijpops, Kurt Jaspers, Wannes Meert | Pruning-Based Extraction of Descriptions from Probabilistic Circuits | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Concept learning is a general task with applications in various domains. As a
motivating example we consider the application of music playlist generation,
where a playlist is represented as a concept (e.g., `relaxing music') rather
than as a fixed collection of songs. In this work we use a probabilistic
circuit to learn a concept from positively labelled and unlabelled examples.
While these circuits form an attractive tractable model for this task, it is
challenging for a domain expert to inspect and analyse them, which impedes
their use within certain applications. We propose to resolve this by converting
a learned probabilistic circuit into a logic-based discriminative model that
covers the high density regions of the circuit. That is, those regions the
circuit classifies as certainly being part of the learned concept. As part of
this approach we present two contributions: PUTPUT, an algorithm to prune low
density regions from a probabilistic circuit while considering both the
F1-score and a newly proposed description length that we call aggregated
entropy. Our experiments demonstrate the effectiveness of our approach in
providing discriminative models, outperforming competitors on the music
playlist generation task and similar datasets.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 13:19:45 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Jun 2024 12:54:23 GMT"
}
] | 1,717,632,000,000 | [
[
"Bocklandt",
"Sieben",
""
],
[
"Derkinderen",
"Vincent",
""
],
[
"Vanderstraeten",
"Koen",
""
],
[
"Pijpops",
"Wouter",
""
],
[
"Jaspers",
"Kurt",
""
],
[
"Meert",
"Wannes",
""
]
] |
2311.13577 | Ayush Agrawal | Ayush Agrawal, Raghav Prabhakar, Anirudh Goyal, Dianbo Liu | Physical Reasoning and Object Planning for Household Embodied Agents | Total: 32 pages ( 16 pages main content, 11 Figures) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this study, we explore the sophisticated domain of task planning for
robust household embodied agents, with a particular emphasis on the intricate
task of selecting substitute objects. We introduce the CommonSense Object
Affordance Task (COAT), a novel framework designed to analyze reasoning
capabilities in commonsense scenarios. This approach is centered on
understanding how these agents can effectively identify and utilize alternative
objects when executing household tasks, thereby offering insights into the
complexities of practical decision-making in real-world environments.Drawing
inspiration from human decision-making, we explore how large language models
tackle this challenge through three meticulously crafted commonsense
question-and-answer datasets, featuring refined rules and human annotations.
Our evaluation of state-of-the-art language models on these datasets sheds
light on three pivotal considerations: 1) aligning an object's inherent utility
with the task at hand, 2) navigating contextual dependencies (societal norms,
safety, appropriateness, and efficiency), and 3) accounting for the current
physical state of the object. To maintain accessibility, we introduce five
abstract variables reflecting an object's physical condition, modulated by
human insights to simulate diverse household scenarios. Our contributions
include insightful Object-Utility mappings addressing the first consideration
and two extensive QA datasets (15k and 130k questions) probing the intricacies
of contextual dependencies and object states. The datasets, along with our
findings, are accessible at: \url{https://github.com/com-phy-affordance/COAT}.
This research not only advances our understanding of physical commonsense
reasoning in language models but also paves the way for future improvements in
household agent intelligence.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 18:32:03 GMT"
}
] | 1,700,697,600,000 | [
[
"Agrawal",
"Ayush",
""
],
[
"Prabhakar",
"Raghav",
""
],
[
"Goyal",
"Anirudh",
""
],
[
"Liu",
"Dianbo",
""
]
] |
2311.13712 | Bilge Acun | Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang,
Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia and James
Zou | Data Acquisition: A New Frontier in Data-centric AI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | As Machine Learning (ML) systems continue to grow, the demand for relevant
and comprehensive datasets becomes imperative. There is limited study on the
challenges of data acquisition due to ad-hoc processes and lack of consistent
methodologies. We first present an investigation of current data marketplaces,
revealing lack of platforms offering detailed information about datasets,
transparent pricing, standardized data formats. With the objective of inciting
participation from the data-centric AI community, we then introduce the DAM
challenge, a benchmark to model the interaction between the data providers and
acquirers. The benchmark was released as a part of DataPerf. Our evaluation of
the submitted strategies underlines the need for effective data acquisition
strategies in ML.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 22:15:17 GMT"
}
] | 1,701,043,200,000 | [
[
"Chen",
"Lingjiao",
""
],
[
"Acun",
"Bilge",
""
],
[
"Ardalani",
"Newsha",
""
],
[
"Sun",
"Yifan",
""
],
[
"Kang",
"Feiyang",
""
],
[
"Lyu",
"Hanrui",
""
],
[
"Kwon",
"Yongchan",
""
],
[
"Jia",
"Ruoxi",
""
],
[
"Wu",
"Carole-Jean",
""
],
[
"Zaharia",
"Matei",
""
],
[
"Zou",
"James",
""
]
] |
2311.13720 | Turgay Caglar | Turgay Caglar, Sirine Belhaj, Tathagata Chakraborti, Michael Katz,
Sarath Sreedharan | Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models
for AI Planning | 24 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the first work to look at the application of large language models
(LLMs) for the purpose of model space edits in automated planning tasks. To set
the stage for this union, we explore two different flavors of model space
problems that have been studied in the AI planning literature and explore the
effect of an LLM on those tasks. We empirically demonstrate how the performance
of an LLM contrasts with combinatorial search (CS) -- an approach that has been
traditionally used to solve model space tasks in planning, both with the LLM in
the role of a standalone model space reasoner as well as in the role of a
statistical signal in concert with the CS approach as part of a two-stage
process. Our experiments show promising results suggesting further forays of
LLMs into the exciting world of model space reasoning for planning tasks in the
future.
| [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 22:27:47 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Mar 2024 00:19:24 GMT"
}
] | 1,709,683,200,000 | [
[
"Caglar",
"Turgay",
""
],
[
"Belhaj",
"Sirine",
""
],
[
"Chakraborti",
"Tathagata",
""
],
[
"Katz",
"Michael",
""
],
[
"Sreedharan",
"Sarath",
""
]
] |
2311.13782 | Hao Feng | Hao Feng, Yi Yang, Zhu Han | Scalable AI Generative Content for Vehicular Network Semantic
Communication | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Perceiving vehicles in a driver's blind spot is vital for safe driving. The
detection of potentially dangerous vehicles in these blind spots can benefit
from vehicular network semantic communication technology. However, efficient
semantic communication involves a trade-off between accuracy and delay,
especially in bandwidth-limited situations. This paper unveils a scalable
Artificial Intelligence Generated Content (AIGC) system that leverages an
encoder-decoder architecture. This system converts images into textual
representations and reconstructs them into quality-acceptable images,
optimizing transmission for vehicular network semantic communication. Moreover,
when bandwidth allows, auxiliary information is integrated. The encoder-decoder
aims to maintain semantic equivalence with the original images across various
tasks. Then the proposed approach employs reinforcement learning to enhance the
reliability of the generated contents. Experimental results suggest that the
proposed method surpasses the baseline in perceiving vehicles in blind spots
and effectively compresses communication data. While this method is
specifically designed for driving scenarios, this encoder-decoder architecture
also holds potential for wide use across various semantic communication
scenarios.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 02:57:04 GMT"
}
] | 1,701,043,200,000 | [
[
"Feng",
"Hao",
""
],
[
"Yang",
"Yi",
""
],
[
"Han",
"Zhu",
""
]
] |
2311.13811 | Ling Feng | Ling Feng, Danyang Li, Tianhao Wu, Xuliang Duan | Education distillation:getting student models to learn in shcools | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge distillation is one of the methods for model compression, and
existing knowledge distillation techniques focus on how to improve the
distillation algorithm so as to enhance the distillation efficiency. This paper
introduces dynamic incremental learning into knowledge distillation and
proposes a distillation strategy for education distillation. Specifically, it
is proposed to take fragmented student models divided from the complete student
model as lower-grade models. As the grade level rises, fragmented student
models deepen in conjunction with designed teaching reference layers, while
learning and distilling from more teacher models. By moving from lower to
higher grades, fragmented student models were gradually integrated into a
complete target student model, and the performance of the student models
gradually improved from lower to higher grades of the stage. Education
distillation strategies combined with distillation algorithms outperform the
results of single distillation algorithms on the public dataset
CIFAR100,Caltech256, Food-101 dataset.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 05:20:18 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Nov 2023 02:32:54 GMT"
}
] | 1,701,129,600,000 | [
[
"Feng",
"Ling",
""
],
[
"Li",
"Danyang",
""
],
[
"Wu",
"Tianhao",
""
],
[
"Duan",
"Xuliang",
""
]
] |
2311.13852 | Sumit Dalal | Sumit Dalal, Deepa Tilwani, Kaushik Roy, Manas Gaur, Sarika Jain,
Valerie Shalin, and Amit Sheth | A Cross Attention Approach to Diagnostic Explainability using Clinical
Practice Guidelines for Depression | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The lack of explainability using relevant clinical knowledge hinders the
adoption of Artificial Intelligence-powered analysis of unstructured clinical
dialogue. A wealth of relevant, untapped Mental Health (MH) data is available
in online communities, providing the opportunity to address the explainability
problem with substantial potential impact as a screening tool for both online
and offline applications. We develop a method to enhance attention in popular
transformer models and generate clinician-understandable explanations for
classification by incorporating external clinical knowledge. Inspired by how
clinicians rely on their expertise when interacting with patients, we leverage
relevant clinical knowledge to model patient inputs, providing meaningful
explanations for classification. This will save manual review time and engender
trust. We develop such a system in the context of MH using clinical practice
guidelines (CPG) for diagnosing depression, a mental health disorder of global
concern. We propose an application-specific language model called ProcesS
knowledge-infused cross ATtention (PSAT), which incorporates CPGs when
computing attention. Through rigorous evaluation on three expert-curated
datasets related to depression, we demonstrate application-relevant
explainability of PSAT. PSAT also surpasses the performance of nine baseline
models and can provide explanations where other baselines fall short. We
transform a CPG resource focused on depression, such as the Patient Health
Questionnaire (e.g. PHQ-9) and related questions, into a machine-readable
ontology using SNOMED-CT. With this resource, PSAT enhances the ability of
models like GPT-3.5 to generate application-relevant explanations.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 08:42:18 GMT"
},
{
"version": "v2",
"created": "Sun, 28 Apr 2024 16:41:37 GMT"
}
] | 1,714,435,200,000 | [
[
"Dalal",
"Sumit",
""
],
[
"Tilwani",
"Deepa",
""
],
[
"Roy",
"Kaushik",
""
],
[
"Gaur",
"Manas",
""
],
[
"Jain",
"Sarika",
""
],
[
"Shalin",
"Valerie",
""
],
[
"Sheth",
"Amit",
""
]
] |
2311.13884 | Bin Zhang | Bin Zhang, Hangyu Mao, Jingqing Ruan, Ying Wen, Yang Li, Shao Zhang,
Zhiwei Xu, Dapeng Li, Ziyue Li, Rui Zhao, Lijuan Li, Guoliang Fan | Controlling Large Language Model-based Agents for Large-Scale
Decision-Making: An Actor-Critic Approach | 13 pages, 11 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The remarkable progress in Large Language Models (LLMs) opens up new avenues
for addressing planning and decision-making problems in Multi-Agent Systems
(MAS). However, as the number of agents increases, the issues of hallucination
in LLMs and coordination in MAS have become increasingly prominent.
Additionally, the efficient utilization of tokens emerges as a critical
consideration when employing LLMs to facilitate the interactions among a
substantial number of agents. In this paper, we develop a modular framework
called LLaMAC to mitigate these challenges. LLaMAC implements a value
distribution encoding similar to that found in the human brain, utilizing
internal and external feedback mechanisms to facilitate collaboration and
iterative reasoning among its modules. Through evaluations involving system
resource allocation and robot grid transportation, we demonstrate the
considerable advantages afforded by our proposed approach.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 10:14:58 GMT"
},
{
"version": "v2",
"created": "Sat, 9 Dec 2023 05:24:57 GMT"
},
{
"version": "v3",
"created": "Tue, 23 Jan 2024 14:11:04 GMT"
}
] | 1,706,054,400,000 | [
[
"Zhang",
"Bin",
""
],
[
"Mao",
"Hangyu",
""
],
[
"Ruan",
"Jingqing",
""
],
[
"Wen",
"Ying",
""
],
[
"Li",
"Yang",
""
],
[
"Zhang",
"Shao",
""
],
[
"Xu",
"Zhiwei",
""
],
[
"Li",
"Dapeng",
""
],
[
"Li",
"Ziyue",
""
],
[
"Zhao",
"Rui",
""
],
[
"Li",
"Lijuan",
""
],
[
"Fan",
"Guoliang",
""
]
] |
2311.13905 | Lucas Magnana | Lucas Magnana (AGORA), Herv\'e Rivano (AGORA), Nicolas Chiabaut | A DRL solution to help reduce the cost in waiting time of securing a
traffic light for cyclists | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cyclists prefer to use infrastructure that separates them from motorized
traffic. Using a traffic light to segregate car and bike flows, with the
addition of bike-specific green phases, is a lightweight and cheap solution
that can be deployed dynamically to assess the opportunity of a heavier
infrastructure such as a separate bike lane. To compensate for the increased
waiting time induced by these new phases, we introduce in this paper a deep
reinforcement learning solution that adapts the green phase cycle of a traffic
light to the traffic. Vehicle counter data are used to compare the DRL approach
with the actuated traffic light control algorithm over whole days. Results show
that DRL achieves better minimization of vehicle waiting time at almost all
hours. Our DRL approach is also robust to moderate changes in bike traffic. The
code of this paper is available at
https://github.com/LucasMagnana/A-DRL-solution-to-help-reduce-the-cost-in-waiting-time-of-securing-a-traffic-light-for-cyclists.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 10:39:40 GMT"
},
{
"version": "v2",
"created": "Mon, 8 Apr 2024 13:31:25 GMT"
}
] | 1,712,620,800,000 | [
[
"Magnana",
"Lucas",
"",
"AGORA"
],
[
"Rivano",
"Hervé",
"",
"AGORA"
],
[
"Chiabaut",
"Nicolas",
""
]
] |
2311.13960 | Mohammad Lataifeh | Mohammad Lataifeh, Xavier A Carrascoa, Ashraf M Elnagara, Naveed
Ahmeda, Imran Junejo | Human Machine Co-Creation. A Complementary Cognitive Approach to
Creative Character Design Process Using GANs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in Generative Adversarial Networks GANs applications continue
to attract the attention of researchers in different fields. In such a
framework, two neural networks compete adversely to generate new visual
contents indistinguishable from the original dataset. The objective of this
research is to create a complementary codesign process between humans and
machines to augment character designers abilities in visualizing and creating
new characters for multimedia projects such as games and animation. Driven by
design cognitive scaffolding, the proposed approach aims to inform the process
of perceiving, knowing, and making. The machine generated concepts are used as
a launching platform for character designers to conceptualize new characters. A
labelled dataset of 22,000 characters was developed for this work and deployed
using different GANs to evaluate the most suited for the context, followed by
mixed methods evaluation for the machine output and human derivations. The
discussed results substantiate the value of the proposed cocreation framework
and elucidate how the generated concepts are used as cognitive substances that
interact with designers competencies in a versatile manner to influence the
creative processes of conceptualizing novel characters.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 12:18:39 GMT"
}
] | 1,701,043,200,000 | [
[
"Lataifeh",
"Mohammad",
""
],
[
"Carrascoa",
"Xavier A",
""
],
[
"Elnagara",
"Ashraf M",
""
],
[
"Ahmeda",
"Naveed",
""
],
[
"Junejo",
"Imran",
""
]
] |
2311.14003 | Tian Huang | Tian Huang, Ke Li | Direct Preference-Based Evolutionary Multi-Objective Optimization with
Dueling Bandit | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimization problems find widespread use in both single-objective and
multi-objective scenarios. In practical applications, users aspire for
solutions that converge to the region of interest (ROI) along the Pareto front
(PF). While the conventional approach involves approximating a fitness function
or an objective function to reflect user preferences, this paper explores an
alternative avenue. Specifically, we aim to discover a method that sidesteps
the need for calculating the fitness function, relying solely on human
feedback. Our proposed approach entails conducting direct preference learning
facilitated by an active dueling bandit algorithm. The experimental phase is
structured into three sessions. Firstly, we assess the performance of our
active dueling bandit algorithm. Secondly, we implement our proposed method
within the context of Multi-objective Evolutionary Algorithms (MOEAs). Finally,
we deploy our method in a practical problem, specifically in protein structure
prediction (PSP). This research presents a novel interactive preference-based
MOEA framework that not only addresses the limitations of traditional
techniques but also unveils new possibilities for optimization problems.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 13:38:43 GMT"
}
] | 1,701,043,200,000 | [
[
"Huang",
"Tian",
""
],
[
"Li",
"Ke",
""
]
] |
2311.14057 | Erik B. Terres Escudero | Erik B. Terres Escudero, Danel Arias Alamo, Oier Mentxaka G\'omez,
Pablo Garc\'ia Bringas | Assessing the Impact of Noise on Quantum Neural Networks: An
Experimental Analysis | null | Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in
Computer Science(), vol 14001. Springer, Cham | 10.1007/978-3-031-40725-3_27 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the race towards quantum computing, the potential benefits of quantum
neural networks (QNNs) have become increasingly apparent. However, Noisy
Intermediate-Scale Quantum (NISQ) processors are prone to errors, which poses a
significant challenge for the execution of complex algorithms or quantum
machine learning. To ensure the quality and security of QNNs, it is crucial to
explore the impact of noise on their performance. This paper provides a
comprehensive analysis of the impact of noise on QNNs, examining the Mottonen
state preparation algorithm under various noise models and studying the
degradation of quantum states as they pass through multiple layers of QNNs.
Additionally, the paper evaluates the effect of noise on the performance of
pre-trained QNNs and highlights the challenges posed by noise models in quantum
computing. The findings of this study have significant implications for the
development of quantum software, emphasizing the importance of prioritizing
stability and noise-correction measures when developing QNNs to ensure reliable
and trustworthy results. This paper contributes to the growing body of
literature on quantum computing and quantum machine learning, providing new
insights into the impact of noise on QNNs and paving the way towards the
development of more robust and efficient quantum algorithms.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 15:22:22 GMT"
}
] | 1,701,043,200,000 | [
[
"Escudero",
"Erik B. Terres",
""
],
[
"Alamo",
"Danel Arias",
""
],
[
"Gómez",
"Oier Mentxaka",
""
],
[
"Bringas",
"Pablo García",
""
]
] |
2311.14061 | Pallavi Bagga | Pallavi Bagga, Kostas Stathis | Towards Explainable Strategy Templates using NLP Transformers | null | Workshop Workshop on Explainable AI in Finance, November 27, 2023,
ACM, New York, USA | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper bridges the gap between mathematical heuristic strategies learned
from Deep Reinforcement Learning (DRL) in automated agent negotiation, and
comprehensible, natural language explanations. Our aim is to make these
strategies more accessible to non-experts. By leveraging traditional Natural
Language Processing (NLP) techniques and Large Language Models (LLMs) equipped
with Transformers, we outline how parts of DRL strategies composed of parts
within strategy templates can be transformed into user-friendly, human-like
English narratives. To achieve this, we present a top-level algorithm that
involves parsing mathematical expressions of strategy templates, semantically
interpreting variables and structures, generating rule-based primary
explanations, and utilizing a Generative Pre-trained Transformer (GPT) model to
refine and contextualize these explanations. Subsequent customization for
varied audiences and meticulous validation processes in an example illustrate
the applicability and potential of this approach.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 15:37:19 GMT"
}
] | 1,701,043,200,000 | [
[
"Bagga",
"Pallavi",
""
],
[
"Stathis",
"Kostas",
""
]
] |
2311.14109 | Cheng Tan | Cheng Tan, Jingxuan Wei, Zhangyang Gao, Linzhuang Sun, Siyuan Li,
Xihong Yang, Stan Z. Li | Boosting the Power of Small Multimodal Reasoning Models to Match Larger
Models with Self-Consistency Training | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal reasoning is a challenging task that requires models to reason
across multiple modalities to answer questions. Existing approaches have made
progress by incorporating language and visual modalities into a two-stage
reasoning framework, separating rationale generation from answer inference.
However, these approaches often fall short due to the inadequate quality of the
generated rationales. In this work, we delve into the importance of rationales
in model reasoning. We observe that when rationales are completely accurate,
the model's accuracy significantly improves, highlighting the need for
high-quality rationale generation. Motivated by this, we propose MC-CoT, a
self-consistency training strategy that generates multiple rationales and
answers, subsequently selecting the most accurate through a voting process.
This approach not only enhances the quality of generated rationales but also
leads to more accurate and robust answers. Through extensive experiments, we
demonstrate that our approach significantly improves model performance across
various benchmarks. Remarkably, we show that even smaller base models, when
equipped with our proposed approach, can achieve results comparable to those of
larger models, illustrating the potential of our approach in harnessing the
power of rationales for improved multimodal reasoning. The code is available at
https://github.com/chengtan9907/mc-cot.
| [
{
"version": "v1",
"created": "Thu, 23 Nov 2023 17:09:48 GMT"
}
] | 1,701,043,200,000 | [
[
"Tan",
"Cheng",
""
],
[
"Wei",
"Jingxuan",
""
],
[
"Gao",
"Zhangyang",
""
],
[
"Sun",
"Linzhuang",
""
],
[
"Li",
"Siyuan",
""
],
[
"Yang",
"Xihong",
""
],
[
"Li",
"Stan Z.",
""
]
] |
2311.14270 | Ekaterina Nikonova | Ekaterina Nikonova, Cheng Xue, Jochen Renz | Efficient Open-world Reinforcement Learning via Knowledge Distillation
and Autonomous Rule Discovery | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Deep reinforcement learning suffers from catastrophic forgetting and sample
inefficiency making it less applicable to the ever-changing real world.
However, the ability to use previously learned knowledge is essential for AI
agents to quickly adapt to novelties. Often, certain spatial information
observed by the agent in the previous interactions can be leveraged to infer
task-specific rules. Inferred rules can then help the agent to avoid
potentially dangerous situations in the previously unseen states and guide the
learning process increasing agent's novelty adaptation speed. In this work, we
propose a general framework that is applicable to deep reinforcement learning
agents. Our framework provides the agent with an autonomous way to discover the
task-specific rules in the novel environments and self-supervise it's learning.
We provide a rule-driven deep Q-learning agent (RDQ) as one possible
implementation of that framework. We show that RDQ successfully extracts
task-specific rules as it interacts with the world and uses them to drastically
increase its learning efficiency. In our experiments, we show that the RDQ
agent is significantly more resilient to the novelties than the baseline
agents, and is able to detect and adapt to novel situations faster.
| [
{
"version": "v1",
"created": "Fri, 24 Nov 2023 04:12:50 GMT"
}
] | 1,701,043,200,000 | [
[
"Nikonova",
"Ekaterina",
""
],
[
"Xue",
"Cheng",
""
],
[
"Renz",
"Jochen",
""
]
] |
2311.14315 | Hui Liu | Hui Liu, Wenya Wang, Hao Sun, Anderson Rocha, and Haoliang Li | Robust Domain Misinformation Detection via Multi-modal Feature Alignment | Accepted by TIFS 2023 | null | 10.1109/TIFS.2023.3326368 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Social media misinformation harms individuals and societies and is
potentialized by fast-growing multi-modal content (i.e., texts and images),
which accounts for higher "credibility" than text-only news pieces. Although
existing supervised misinformation detection methods have obtained acceptable
performances in key setups, they may require large amounts of labeled data from
various events, which can be time-consuming and tedious. In turn, directly
training a model by leveraging a publicly available dataset may fail to
generalize due to domain shifts between the training data (a.k.a. source
domains) and the data from target domains. Most prior work on domain shift
focuses on a single modality (e.g., text modality) and ignores the scenario
where sufficient unlabeled target domain data may not be readily available in
an early stage. The lack of data often happens due to the dynamic propagation
trend (i.e., the number of posts related to fake news increases slowly before
catching the public attention). We propose a novel robust domain and
cross-modal approach (\textbf{RDCM}) for multi-modal misinformation detection.
It reduces the domain shift by aligning the joint distribution of textual and
visual modalities through an inter-domain alignment module and bridges the
semantic gap between both modalities through a cross-modality alignment module.
We also propose a framework that simultaneously considers application scenarios
of domain generalization (in which the target domain data is unavailable) and
domain adaptation (in which unlabeled target domain data is available).
Evaluation results on two public multi-modal misinformation detection datasets
(Pheme and Twitter Datasets) evince the superiority of the proposed model. The
formal implementation of this paper can be found in this link:
https://github.com/less-and-less-bugs/RDCM
| [
{
"version": "v1",
"created": "Fri, 24 Nov 2023 07:06:16 GMT"
}
] | 1,701,043,200,000 | [
[
"Liu",
"Hui",
""
],
[
"Wang",
"Wenya",
""
],
[
"Sun",
"Hao",
""
],
[
"Rocha",
"Anderson",
""
],
[
"Li",
"Haoliang",
""
]
] |
2311.14401 | Ana Fern\'andez Vilas | Pablo Garc\'ia Santaclara and Ana Fern\'andez Vilas and Rebeca P.
D\'iaz Redondo | Prototype of deployment of Federated Learning with IoT devices | null | Proceedings of the 19th ACM International Symposium on Performance
Evaluation of Wireless Ad Hoc, Sensor, Ubiquitous Networks. October 2022.
Pages 9-16 | 10.1145/3551663.3558681 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the age of technology, data is an increasingly important resource. This
importance is growing in the field of Artificial Intelligence (AI), where sub
fields such as Machine Learning (ML) need more and more data to achieve better
results. Internet of Things (IoT) is the connection of sensors and smart
objects to collect and exchange data, in addition to achieving many other
tasks. A huge amount of the resource desired, data, is stored in mobile
devices, sensors and other Internet of Things (IoT) devices, but remains there
due to data protection restrictions. At the same time these devices do not have
enough data or computational capacity to train good models. Moreover,
transmitting, storing and processing all this data on a centralised server is
problematic. Federated Learning (FL) provides an innovative solution that
allows devices to learn in a collaborative way. More importantly, it
accomplishes this without violating data protection laws. FL is currently
growing, and there are several solutions that implement it. This article
presents a prototype of a FL solution where the IoT devices used were raspberry
pi boards. The results compare the performance of a solution of this type with
those obtained in traditional approaches. In addition, the FL solution
performance was tested in a hostile environment. A convolutional neural network
(CNN) and a image data set were used. The results show the feasibility and
usability of these techniques, although in many cases they do not reach the
performance of traditional approaches.
| [
{
"version": "v1",
"created": "Fri, 24 Nov 2023 10:37:30 GMT"
}
] | 1,701,043,200,000 | [
[
"Santaclara",
"Pablo García",
""
],
[
"Vilas",
"Ana Fernández",
""
],
[
"Redondo",
"Rebeca P. Díaz",
""
]
] |
2311.14426 | Xiuxin Xia | Xiuxin Xia, Yuchen Guo, Yanwei Wang, Yuchao Yang, Yan Shi and Hong Men | Human-Machine Cooperative Multimodal Learning Method for Cross-subject
Olfactory Preference Recognition | 14 pages, 13 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Odor sensory evaluation has a broad application in food, clothing, cosmetics,
and other fields. Traditional artificial sensory evaluation has poor
repeatability, and the machine olfaction represented by the electronic nose
(E-nose) is difficult to reflect human feelings. Olfactory electroencephalogram
(EEG) contains odor and individual features associated with human olfactory
preference, which has unique advantages in odor sensory evaluation. However,
the difficulty of cross-subject olfactory EEG recognition greatly limits its
application. It is worth noting that E-nose and olfactory EEG are more
advantageous in representing odor information and individual emotions,
respectively. In this paper, an E-nose and olfactory EEG multimodal learning
method is proposed for cross-subject olfactory preference recognition. Firstly,
the olfactory EEG and E-nose multimodal data acquisition and preprocessing
paradigms are established. Secondly, a complementary multimodal data mining
strategy is proposed to effectively mine the common features of multimodal data
representing odor information and the individual features in olfactory EEG
representing individual emotional information. Finally, the cross-subject
olfactory preference recognition is achieved in 24 subjects by fusing the
extracted common and individual features, and the recognition effect is
superior to the state-of-the-art recognition methods. Furthermore, the
advantages of the proposed method in cross-subject olfactory preference
recognition indicate its potential for practical odor evaluation applications.
| [
{
"version": "v1",
"created": "Fri, 24 Nov 2023 11:59:11 GMT"
}
] | 1,701,043,200,000 | [
[
"Xia",
"Xiuxin",
""
],
[
"Guo",
"Yuchen",
""
],
[
"Wang",
"Yanwei",
""
],
[
"Yang",
"Yuchao",
""
],
[
"Shi",
"Yan",
""
],
[
"Men",
"Hong",
""
]
] |
2311.14457 | Zicong Zhao | Zicong Zhao | How to ensure a safe control strategy? Towards a SRL for urban transit
autonomous operation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep reinforcement learning has gradually shown its latent decision-making
ability in urban rail transit autonomous operation. However, since
reinforcement learning can not neither guarantee safety during learning nor
execution, this is still one of the major obstacles to the practical
application of reinforcement learning. Given this drawback, reinforcement
learning applied in the safety-critical autonomous operation domain remains
challenging without generating a safe control command sequence that avoids
overspeed operations. Therefore, a SSA-DRL framework is proposed in this paper
for safe intelligent control of urban rail transit autonomous operation trains.
The proposed framework is combined with linear temporal logic, reinforcement
learning and Monte Carlo tree search and consists of four mainly module: a
post-posed shielding, a searching tree module, a DRL framework and an
additional actor. Furthermore, the output of the framework can meet speed
constraint, schedule constraint and optimize the operation process. Finally,
the proposed SSA-DRL framework for decision-making in urban rail transit
autonomous operation is evaluated in sixteen different sections, and its
effectiveness is demonstrated through an ablation experiment and comparison
with the scheduled operation plan.
| [
{
"version": "v1",
"created": "Fri, 24 Nov 2023 13:11:07 GMT"
},
{
"version": "v2",
"created": "Sat, 6 Jan 2024 09:13:29 GMT"
}
] | 1,704,758,400,000 | [
[
"Zhao",
"Zicong",
""
]
] |
2311.15162 | Zikai Xie | Zikai Xie, Xenophon Evangelopoulos, Joseph Thacker, Andrew Cooper | Domain Knowledge Injection in Bayesian Search for New Materials | 8 pages, 5 figures, published in ECAI23 | Twenty-sixth European Conference on Artificial Intelligence (ECAI
2023) | 10.3233/FAIA230587 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this paper we propose DKIBO, a Bayesian optimization (BO) algorithm that
accommodates domain knowledge to tune exploration in the search space. Bayesian
optimization has recently emerged as a sample-efficient optimizer for many
intractable scientific problems. While various existing BO frameworks allow the
input of prior beliefs to accelerate the search by narrowing down the space,
incorporating such knowledge is not always straightforward and can often
introduce bias and lead to poor performance. Here we propose a simple approach
to incorporate structural knowledge in the acquisition function by utilizing an
additional deterministic surrogate model to enrich the approximation power of
the Gaussian process. This is suitably chosen according to structural
information of the problem at hand and acts a corrective term towards a
better-informed sampling. We empirically demonstrate the practical utility of
the proposed method by successfully injecting domain knowledge in a materials
design task. We further validate our method's performance on different
experimental settings and ablation analyses.
| [
{
"version": "v1",
"created": "Sun, 26 Nov 2023 01:55:55 GMT"
}
] | 1,701,129,600,000 | [
[
"Xie",
"Zikai",
""
],
[
"Evangelopoulos",
"Xenophon",
""
],
[
"Thacker",
"Joseph",
""
],
[
"Cooper",
"Andrew",
""
]
] |
2311.15209 | Wenhao Chai | Zhonghan Zhao, Wenhao Chai, Xuan Wang, Li Boyi, Shengyu Hao, Shidong
Cao, Tian Ye, Jenq-Neng Hwang, Gaoang Wang | See and Think: Embodied Agent in Virtual Environment | Preprint. First three authors contribute equally to this work.
Project Website https://rese1f.github.io/STEVE/ | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs) have achieved impressive progress on several
open-world tasks. Recently, using LLMs to build embodied agents has been a
hotspot. In this paper, we propose STEVE, a comprehensive and visionary
embodied agent in the Minecraft virtual environment. STEVE consists of three
key components: vision perception, language instruction, and code action.
Vision perception involves the interpretation of visual information in the
environment, which is then integrated into the LLMs component with agent state
and task instruction. Language instruction is responsible for iterative
reasoning and decomposing complex tasks into manageable guidelines. Code action
generates executable skill actions based on retrieval in skill database,
enabling the agent to interact effectively within the Minecraft environment. We
also collect STEVE-21K dataset, which includes 600$+$ vision-environment pairs,
20K knowledge question-answering pairs, and 200$+$ skill-code pairs. We conduct
continuous block search, knowledge question and answering, and tech tree
mastery to evaluate the performance. Extensive experiments show that STEVE
achieves at most $1.5 \times$ faster unlocking key tech trees and $2.5 \times$
quicker in block search tasks compared to previous state-of-the-art methods.
| [
{
"version": "v1",
"created": "Sun, 26 Nov 2023 06:38:16 GMT"
},
{
"version": "v2",
"created": "Sun, 3 Dec 2023 00:58:47 GMT"
}
] | 1,701,734,400,000 | [
[
"Zhao",
"Zhonghan",
""
],
[
"Chai",
"Wenhao",
""
],
[
"Wang",
"Xuan",
""
],
[
"Boyi",
"Li",
""
],
[
"Hao",
"Shengyu",
""
],
[
"Cao",
"Shidong",
""
],
[
"Ye",
"Tian",
""
],
[
"Hwang",
"Jenq-Neng",
""
],
[
"Wang",
"Gaoang",
""
]
] |
2311.15920 | Jianxiong Li | Jianxiong Li, Shichao Lin, Tianyu Shi, Chujie Tian, Yu Mei, Jian Song,
Xianyuan Zhan, Ruimin Li | A Fully Data-Driven Approach for Realistic Traffic Signal Control Using
Offline Reinforcement Learning | 15 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The optimization of traffic signal control (TSC) is critical for an efficient
transportation system. In recent years, reinforcement learning (RL) techniques
have emerged as a popular approach for TSC and show promising results for
highly adaptive control. However, existing RL-based methods suffer from notably
poor real-world applicability and hardly have any successful deployments. The
reasons for such failures are mostly due to the reliance on over-idealized
traffic simulators for policy optimization, as well as using unrealistic
fine-grained state observations and reward signals that are not directly
obtainable from real-world sensors. In this paper, we propose a fully
Data-Driven and simulator-free framework for realistic Traffic Signal Control
(D2TSC). Specifically, we combine well-established traffic flow theory with
machine learning to construct a reward inference model to infer the reward
signals from coarse-grained traffic data. With the inferred rewards, we further
propose a sample-efficient offline RL method to enable direct signal control
policy learning from historical offline datasets of real-world intersections.
To evaluate our approach, we collect historical traffic data from a real-world
intersection, and develop a highly customized simulation environment that
strictly follows real data characteristics. We demonstrate through extensive
experiments that our approach achieves superior performance over conventional
and offline RL baselines, and also enjoys much better real-world applicability.
| [
{
"version": "v1",
"created": "Mon, 27 Nov 2023 15:29:21 GMT"
}
] | 1,701,129,600,000 | [
[
"Li",
"Jianxiong",
""
],
[
"Lin",
"Shichao",
""
],
[
"Shi",
"Tianyu",
""
],
[
"Tian",
"Chujie",
""
],
[
"Mei",
"Yu",
""
],
[
"Song",
"Jian",
""
],
[
"Zhan",
"Xianyuan",
""
],
[
"Li",
"Ruimin",
""
]
] |
2311.15933 | Qixiao Hu | Qixiao Hu, Shiquan Zhang, Chaolang Hu, Yuetong Liu | A new fuzzy multi-attribute group decision-making method based on TOPSIS
and optimization models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a new method based on TOPSIS and optimization models is
proposed for multi-attribute group decision-making in the environment of
interval-valued intuitionistic fuzzy sets.Firstly, by minimizing the sum of
differences between individual evaluations and the overallconsistent
evaluations of all experts, a new optimization model is established for
determining expert weights. Secondly, based on TOPSIS method, the improved
closeness index for evaluating each alternative is obtained. Finally, the
attribute weight is determined by establishing an optimization model with the
goal of maximizing the closeness of each alternative, and it is brought into
the closeness index so that the alternatives can be ranked. Combining all these
together, the complete fuzzy multi-attribute group decision-making algorithm is
formulated, which can give full play to the advantages of subjective and
objective weighting methods. In the end, the feasibility and effectiveness of
the provided method are verified by a real case study.
| [
{
"version": "v1",
"created": "Mon, 27 Nov 2023 15:41:30 GMT"
}
] | 1,701,129,600,000 | [
[
"Hu",
"Qixiao",
""
],
[
"Zhang",
"Shiquan",
""
],
[
"Hu",
"Chaolang",
""
],
[
"Liu",
"Yuetong",
""
]
] |
2311.16534 | Xiangguo Sun | Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, Jia Li | Graph Prompt Learning: A Comprehensive Survey and Beyond | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial General Intelligence (AGI) has revolutionized numerous fields, yet
its integration with graph data, a cornerstone in our interconnected world,
remains nascent. This paper presents a pioneering survey on the emerging domain
of graph prompts in AGI, addressing key challenges and opportunities in
harnessing graph data for AGI applications. Despite substantial advancements in
AGI across natural language processing and computer vision, the application to
graph data is relatively underexplored. This survey critically evaluates the
current landscape of AGI in handling graph data, highlighting the distinct
challenges in cross-modality, cross-domain, and cross-task applications
specific to graphs. Our work is the first to propose a unified framework for
understanding graph prompt learning, offering clarity on prompt tokens, token
structures, and insertion patterns in the graph domain. We delve into the
intrinsic properties of graph prompts, exploring their flexibility,
expressiveness, and interplay with existing graph models. A comprehensive
taxonomy categorizes over 100 works in this field, aligning them with
pre-training tasks across node-level, edge-level, and graph-level objectives.
Additionally, we present, ProG, a Python library, and an accompanying website,
to support and advance research in graph prompting. The survey culminates in a
discussion of current challenges and future directions, offering a roadmap for
research in graph prompting within AGI. Through this comprehensive analysis, we
aim to catalyze further exploration and practical applications of AGI in graph
data, underlining its potential to reshape AGI fields and beyond. ProG and the
website can be accessed by
\url{https://github.com/WxxShirley/Awesome-Graph-Prompt}, and
\url{https://github.com/sheldonresearch/ProG}, respectively.
| [
{
"version": "v1",
"created": "Tue, 28 Nov 2023 05:36:59 GMT"
}
] | 1,701,216,000,000 | [
[
"Sun",
"Xiangguo",
""
],
[
"Zhang",
"Jiawen",
""
],
[
"Wu",
"Xixi",
""
],
[
"Cheng",
"Hong",
""
],
[
"Xiong",
"Yun",
""
],
[
"Li",
"Jia",
""
]
] |
2311.16781 | David Milec | David Milec, Viliam Lis\'y, Christopher Kiekintveld | Generation of Games for Opponent Model Differentiation | 4 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Protecting against adversarial attacks is a common multiagent problem.
Attackers in the real world are predominantly human actors, and the protection
methods often incorporate opponent models to improve the performance when
facing humans. Previous results show that modeling human behavior can
significantly improve the performance of the algorithms. However, modeling
humans correctly is a complex problem, and the models are often simplified and
assume humans make mistakes according to some distribution or train parameters
for the whole population from which they sample. In this work, we use data
gathered by psychologists who identified personality types that increase the
likelihood of performing malicious acts. However, in the previous work, the
tests on a handmade game could not show strategic differences between the
models. We created a novel model that links its parameters to psychological
traits. We optimized over parametrized games and created games in which the
differences are profound. Our work can help with automatic game generation when
we need a game in which some models will behave differently and to identify
situations in which the models do not align.
| [
{
"version": "v1",
"created": "Tue, 28 Nov 2023 13:45:03 GMT"
}
] | 1,701,216,000,000 | [
[
"Milec",
"David",
""
],
[
"Lisý",
"Viliam",
""
],
[
"Kiekintveld",
"Christopher",
""
]
] |
2311.16807 | Yaoquanj Wei | Yaoquan Wei, Shunyu Liu, Jie Song, Tongya Zheng, Kaixuan Chen, Yong
Wang, Mingli Song | Agent-Aware Training for Agent-Agnostic Action Advising in Deep
Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Action advising endeavors to leverage supplementary guidance from expert
teachers to alleviate the issue of sampling inefficiency in Deep Reinforcement
Learning (DRL). Previous agent-specific action advising methods are hindered by
imperfections in the agent itself, while agent-agnostic approaches exhibit
limited adaptability to the learning agent. In this study, we propose a novel
framework called Agent-Aware trAining yet Agent-Agnostic Action Advising (A7)
to strike a balance between the two. The underlying concept of A7 revolves
around utilizing the similarity of state features as an indicator for
soliciting advice. However, unlike prior methodologies, the measurement of
state feature similarity is performed by neither the error-prone learning agent
nor the agent-agnostic advisor. Instead, we employ a proxy model to extract
state features that are both discriminative (adaptive to the agent) and
generally applicable (robust to agent noise). Furthermore, we utilize behavior
cloning to train a model for reusing advice and introduce an intrinsic reward
for the advised samples to incentivize the utilization of expert guidance.
Experiments are conducted on the GridWorld, LunarLander, and six prominent
scenarios from Atari games. The results demonstrate that A7 significantly
accelerates the learning process and surpasses existing methods (both
agent-specific and agent-agnostic) by a substantial margin. Our code will be
made publicly available.
| [
{
"version": "v1",
"created": "Tue, 28 Nov 2023 14:09:43 GMT"
}
] | 1,701,216,000,000 | [
[
"Wei",
"Yaoquan",
""
],
[
"Liu",
"Shunyu",
""
],
[
"Song",
"Jie",
""
],
[
"Zheng",
"Tongya",
""
],
[
"Chen",
"Kaixuan",
""
],
[
"Wang",
"Yong",
""
],
[
"Song",
"Mingli",
""
]
] |
2311.17393 | Jaime Carrasco Barra | David Palacios-Meneses, Jaime Carrasco, Sebasti\'an D\'avila,
Maximiliano Mart\'inez, Rodrigo Mahaluf, and Andr\'es Weintraub | Comparison of metaheuristics for the firebreak placement problem: a
simulation-based optimization approach | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The problem of firebreak placement is crucial for fire prevention, and its
effectiveness at landscape scale will depend on their ability to impede the
progress of future wildfires. To provide an adequate response, it is therefore
necessary to consider the stochastic nature of fires, which are highly
unpredictable from ignition to extinction. Thus, the placement of firebreaks
can be considered a stochastic optimization problem where: (1) the objective
function is to minimize the expected cells burnt of the landscape; (2) the
decision variables being the location of firebreaks; and (3) the random
variable being the spatial propagation/behavior of fires. In this paper, we
propose a solution approach for the problem from the perspective of
simulation-based optimization (SbO), where the objective function is not
available (a black-box function), but can be computed (and/or approximated) by
wildfire simulations. For this purpose, Genetic Algorithm and GRASP are
implemented. The final implementation yielded favorable results for the Genetic
Algorithm, demonstrating strong performance in scenarios with medium to high
operational capacity, as well as medium levels of stochasticity
| [
{
"version": "v1",
"created": "Wed, 29 Nov 2023 06:45:07 GMT"
}
] | 1,701,302,400,000 | [
[
"Palacios-Meneses",
"David",
""
],
[
"Carrasco",
"Jaime",
""
],
[
"Dávila",
"Sebastián",
""
],
[
"Martínez",
"Maximiliano",
""
],
[
"Mahaluf",
"Rodrigo",
""
],
[
"Weintraub",
"Andrés",
""
]
] |
2311.17541 | Xu Zhang | Bo Qiao, Liqun Li, Xu Zhang, Shilin He, Yu Kang, Chaoyun Zhang,
Fangkai Yang, Hang Dong, Jue Zhang, Lu Wang, Minghua Ma, Pu Zhao, Si Qin,
Xiaoting Qin, Chao Du, Yong Xu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang | TaskWeaver: A Code-First Agent Framework | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have shown impressive abilities in natural
language understanding and generation, leading to their use in applications
such as chatbots and virtual assistants. However, existing LLM frameworks face
limitations in handling domain-specific data analytics tasks with rich data
structures. Moreover, they struggle with flexibility to meet diverse user
requirements. To address these issues, TaskWeaver is proposed as a code-first
framework for building LLM-powered autonomous agents. It converts user requests
into executable code and treats user-defined plugins as callable functions.
TaskWeaver provides support for rich data structures, flexible plugin usage,
and dynamic plugin selection, and leverages LLM coding capabilities for complex
logic. It also incorporates domain-specific knowledge through examples and
ensures the secure execution of generated code. TaskWeaver offers a powerful
and flexible framework for creating intelligent conversational agents that can
handle complex tasks and adapt to domain-specific scenarios. The code is
open-sourced at https://github.com/microsoft/TaskWeaver/.
| [
{
"version": "v1",
"created": "Wed, 29 Nov 2023 11:23:42 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Dec 2023 07:42:56 GMT"
}
] | 1,701,648,000,000 | [
[
"Qiao",
"Bo",
""
],
[
"Li",
"Liqun",
""
],
[
"Zhang",
"Xu",
""
],
[
"He",
"Shilin",
""
],
[
"Kang",
"Yu",
""
],
[
"Zhang",
"Chaoyun",
""
],
[
"Yang",
"Fangkai",
""
],
[
"Dong",
"Hang",
""
],
[
"Zhang",
"Jue",
""
],
[
"Wang",
"Lu",
""
],
[
"Ma",
"Minghua",
""
],
[
"Zhao",
"Pu",
""
],
[
"Qin",
"Si",
""
],
[
"Qin",
"Xiaoting",
""
],
[
"Du",
"Chao",
""
],
[
"Xu",
"Yong",
""
],
[
"Lin",
"Qingwei",
""
],
[
"Rajmohan",
"Saravan",
""
],
[
"Zhang",
"Dongmei",
""
]
] |
2311.17766 | Bernd Bassimir | Bernd Bassimir, Rolf Wanka | Robustness Approaches for the Examination Timetabling Problem under Data
Uncertainty | original paper: 15 pages, published at the Multidisciplinary
International Scheduling Conference 2019 (MISTA 2019) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the literature the examination timetabling problem (ETTP) is often
considered a post-enrollment problem (PE-ETTP). In the real world, universities
often schedule their exams before students register using information from
previous terms. A direct consequence of this approach is the uncertainty
present in the resulting models. In this work we discuss several approaches
available in the robust optimization literature. We consider the implications
of each approach in respect to the examination timetabling problem and present
how the most favorable approaches can be applied to the ETTP. Afterwards we
analyze the impact of some possible implementations of the given robustness
approaches on two real world instances and several random instances generated
by our instance generation framework which we introduce in this work.
| [
{
"version": "v1",
"created": "Wed, 29 Nov 2023 16:06:17 GMT"
}
] | 1,701,302,400,000 | [
[
"Bassimir",
"Bernd",
""
],
[
"Wanka",
"Rolf",
""
]
] |
2311.17822 | Seyedeh Gol Ara Ghoreishi | Seyedeh Gol Ara Ghoreishi, Sonia Moshfeghi, Muhammad Tanveer Jan,
Joshua Conniff, KwangSoo Yang, Jinwoo Jang, Borko Furht, Ruth Tappen, David
Newman, Monica Rosselli, Jiannan Zhai | Anomalous Behavior Detection in Trajectory Data of Older Drivers | IEEE HONET 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Given a road network and a set of trajectory data, the anomalous behavior
detection (ABD) problem is to identify drivers that show significant
directional deviations, hardbrakings, and accelerations in their trips. The ABD
problem is important in many societal applications, including Mild Cognitive
Impairment (MCI) detection and safe route recommendations for older drivers.
The ABD problem is computationally challenging due to the large size of
temporally-detailed trajectories dataset. In this paper, we propose an
Edge-Attributed Matrix that can represent the key properties of
temporally-detailed trajectory datasets and identify abnormal driving
behaviors. Experiments using real-world datasets demonstrated that our approach
identifies abnormal driving behaviors.
| [
{
"version": "v1",
"created": "Wed, 29 Nov 2023 17:22:28 GMT"
}
] | 1,701,302,400,000 | [
[
"Ghoreishi",
"Seyedeh Gol Ara",
""
],
[
"Moshfeghi",
"Sonia",
""
],
[
"Jan",
"Muhammad Tanveer",
""
],
[
"Conniff",
"Joshua",
""
],
[
"Yang",
"KwangSoo",
""
],
[
"Jang",
"Jinwoo",
""
],
[
"Furht",
"Borko",
""
],
[
"Tappen",
"Ruth",
""
],
[
"Newman",
"David",
""
],
[
"Rosselli",
"Monica",
""
],
[
"Zhai",
"Jiannan",
""
]
] |
2311.18644 | Carlos Correa | Carlos G. Correa, Sophia Sanborn, Mark K. Ho, Frederick Callaway,
Nathaniel D. Daw, and Thomas L. Griffiths | Exploring the hierarchical structure of human plans via program
generation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Human behavior is inherently hierarchical, resulting from the decomposition
of a task into subtasks or an abstract action into concrete actions. However,
behavior is typically measured as a sequence of actions, which makes it
difficult to infer its hierarchical structure. In this paper, we explore how
people form hierarchically-structured plans, using an experimental paradigm
that makes hierarchical representations observable: participants create
programs that produce sequences of actions in a language with explicit
hierarchical structure. This task lets us test two well-established principles
of human behavior: utility maximization (i.e. using fewer actions) and minimum
description length (MDL; i.e. having a shorter program). We find that humans
are sensitive to both metrics, but that both accounts fail to predict a
qualitative feature of human-created programs, namely that people prefer
programs with reuse over and above the predictions of MDL. We formalize this
preference for reuse by extending the MDL account into a generative model over
programs, modeling hierarchy choice as the induction of a grammar over actions.
Our account can explain the preference for reuse and provides the best
prediction of human behavior, going beyond simple accounts of compressibility
to highlight a principle that guides hierarchical planning.
| [
{
"version": "v1",
"created": "Thu, 30 Nov 2023 15:53:02 GMT"
}
] | 1,701,388,800,000 | [
[
"Correa",
"Carlos G.",
""
],
[
"Sanborn",
"Sophia",
""
],
[
"Ho",
"Mark K.",
""
],
[
"Callaway",
"Frederick",
""
],
[
"Daw",
"Nathaniel D.",
""
],
[
"Griffiths",
"Thomas L.",
""
]
] |
2311.18662 | Daniel Fuertes | Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar, Narciso
Garc\'ia | Solving the Team Orienteering Problem with Transformers | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Route planning for a fleet of vehicles is an important task in applications
such as package delivery, surveillance, or transportation. This problem is
usually modeled as a Combinatorial Optimization problem named as Team
Orienteering Problem. The most popular Team Orienteering Problem solvers are
mainly based on either linear programming, which provides accurate solutions by
employing a large computation time that grows with the size of the problem, or
heuristic methods, which usually find suboptimal solutions in a shorter amount
of time. In this paper, a multi-agent route planning system capable of solving
the Team Orienteering Problem in a very fast and accurate manner is presented.
The proposed system is based on a centralized Transformer neural network that
can learn to encode the scenario (modeled as a graph) and the context of the
agents to provide fast and accurate solutions. Several experiments have been
performed to demonstrate that the presented system can outperform most of the
state-of-the-art works in terms of computation speed. In addition, the code is
publicly available at http://gti.ssr.upm.es/data.
| [
{
"version": "v1",
"created": "Thu, 30 Nov 2023 16:10:35 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Dec 2023 09:48:02 GMT"
}
] | 1,701,648,000,000 | [
[
"Fuertes",
"Daniel",
""
],
[
"del-Blanco",
"Carlos R.",
""
],
[
"Jaureguizar",
"Fernando",
""
],
[
"García",
"Narciso",
""
]
] |
2312.00332 | Peng Wang | Peng Wang | Matching Weak Informative Ontologies | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Most existing ontology matching methods utilize the literal information to
discover alignments. However, some literal information in ontologies may be
opaque and some ontologies may not have sufficient literal information. In this
paper, these ontologies are named as weak informative ontologies (WIOs) and it
is challenging for existing methods to matching WIOs. On one hand, string-based
and linguistic-based matching methods cannot work well for WIOs. On the other
hand, some matching methods use external resources to improve their
performance, but collecting and processing external resources is still
time-consuming. To address this issue, this paper proposes a practical method
for matching WIOs by employing the ontology structure information to discover
alignments. First, the semantic subgraphs are extracted from the ontology graph
to capture the precise meanings of ontology elements. Then, a new similarity
propagation model is designed for matching WIOs. Meanwhile, in order to avoid
meaningless propagation, the similarity propagation is constrained by semantic
subgraphs and other conditions. Consequently, the similarity propagation model
ensures a balance between efficiency and quality during matching. Finally, the
similarity propagation model uses a few credible alignments as seeds to find
more alignments, and some useful strategies are adopted to improve the
performance. This matching method for WIOs has been implemented in the ontology
matching system Lily. Experimental results on public OAEI benchmark datasets
demonstrate that Lily significantly outperforms most of the state-of-the-art
works in both WIO matching tasks and general ontology matching tasks. In
particular, Lily increases the recall by a large margin, while it still obtains
high precision of matching results.
| [
{
"version": "v1",
"created": "Fri, 1 Dec 2023 03:56:29 GMT"
}
] | 1,701,648,000,000 | [
[
"Wang",
"Peng",
""
]
] |
2312.00380 | Georgios Makridis | Georgios Makridis, Vasileios Koukos, Georgios Fatouros, Dimosthenis
Kyriazis | Enhancing Explainability in Mobility Data Science through a combination
of methods | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In the domain of Mobility Data Science, the intricate task of interpreting
models trained on trajectory data, and elucidating the spatio-temporal movement
of entities, has persistently posed significant challenges. Conventional XAI
techniques, although brimming with potential, frequently overlook the distinct
structure and nuances inherent within trajectory data. Observing this
deficiency, we introduced a comprehensive framework that harmonizes pivotal XAI
techniques: LIME (Local Interpretable Model-agnostic Explanations), SHAP
(SHapley Additive exPlanations), Saliency maps, attention mechanisms, direct
trajectory visualization, and Permutation Feature Importance (PFI). Unlike
conventional strategies that deploy these methods singularly, our unified
approach capitalizes on the collective efficacy of these techniques, yielding
deeper and more granular insights for models reliant on trajectory data. In
crafting this synthesis, we effectively address the multifaceted essence of
trajectories, achieving not only amplified interpretability but also a nuanced,
contextually rich comprehension of model decisions. To validate and enhance our
framework, we undertook a survey to gauge preferences and reception among
various user demographics. Our findings underscored a dichotomy: professionals
with academic orientations, particularly those in roles like Data Scientist, IT
Expert, and ML Engineer, showcased a profound, technical understanding and
often exhibited a predilection for amalgamated methods for interpretability.
Conversely, end-users or individuals less acquainted with AI and Data Science
showcased simpler inclinations, such as bar plots indicating timestep
significance or visual depictions pinpointing pivotal segments of a vessel's
trajectory.
| [
{
"version": "v1",
"created": "Fri, 1 Dec 2023 07:09:21 GMT"
}
] | 1,701,648,000,000 | [
[
"Makridis",
"Georgios",
""
],
[
"Koukos",
"Vasileios",
""
],
[
"Fatouros",
"Georgios",
""
],
[
"Kyriazis",
"Dimosthenis",
""
]
] |
2312.00746 | Haochen Shi | Dekun Wu, Haochen Shi, Zhiyuan Sun, Bang Liu | Deciphering Digital Detectives: Understanding LLM Behaviors and
Capabilities in Multi-Agent Mystery Games | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study, we explore the application of Large Language Models (LLMs) in
\textit{Jubensha}, a Chinese detective role-playing game and a novel area in
Artificial Intelligence (AI) driven gaming. We introduce the first dataset
specifically for Jubensha, including character scripts and game rules, to
foster AI agent development in this complex narrative environment. Our work
also presents a unique multi-agent interaction framework using LLMs, allowing
AI agents to autonomously engage in this game. To evaluate the gaming
performance of these AI agents, we developed novel methods measuring their
mastery of case information and reasoning skills. Furthermore, we incorporated
the latest advancements in in-context learning to improve the agents'
performance in information gathering, murderer identification, and logical
reasoning. The experimental results validate the effectiveness of our proposed
methods. This work aims to offer a novel perspective on understanding LLM
capabilities and establish a new benchmark for evaluating large language
model-based agents.
| [
{
"version": "v1",
"created": "Fri, 1 Dec 2023 17:33:57 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Feb 2024 06:24:28 GMT"
}
] | 1,709,251,200,000 | [
[
"Wu",
"Dekun",
""
],
[
"Shi",
"Haochen",
""
],
[
"Sun",
"Zhiyuan",
""
],
[
"Liu",
"Bang",
""
]
] |
2312.00798 | Matthew O. Jackson | Qiaozhu Mei, Yutong Xie, Walter Yuan, Matthew O. Jackson | A Turing Test: Are AI Chatbots Behaviorally Similar to Humans? | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We administer a Turing Test to AI Chatbots. We examine how Chatbots behave in
a suite of classic behavioral games that are designed to elicit characteristics
such as trust, fairness, risk-aversion, cooperation, \textit{etc.}, as well as
how they respond to a traditional Big-5 psychological survey that measures
personality traits. ChatGPT-4 exhibits behavioral and personality traits that
are statistically indistinguishable from a random human from tens of thousands
of human subjects from more than 50 countries. Chatbots also modify their
behavior based on previous experience and contexts ``as if'' they were learning
from the interactions, and change their behavior in response to different
framings of the same strategic situation. Their behaviors are often distinct
from average and modal human behaviors, in which case they tend to behave on
the more altruistic and cooperative end of the distribution. We estimate that
they act as if they are maximizing an average of their own and partner's
payoffs.
| [
{
"version": "v1",
"created": "Sun, 19 Nov 2023 16:44:09 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Jan 2024 18:43:29 GMT"
}
] | 1,704,153,600,000 | [
[
"Mei",
"Qiaozhu",
""
],
[
"Xie",
"Yutong",
""
],
[
"Yuan",
"Walter",
""
],
[
"Jackson",
"Matthew O.",
""
]
] |
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