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2401.12467 | Yuliang Liu | Haisu Guan, Jinpeng Wan, Yuliang Liu, Pengjie Wang, Kaile Zhang,
Zhebin Kuang, Xinyu Wang, Xiang Bai, Lianwen Jin | An open dataset for the evolution of oracle bone characters: EVOBC | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The earliest extant Chinese characters originate from oracle bone
inscriptions, which are closely related to other East Asian languages. These
inscriptions hold immense value for anthropology and archaeology. However,
deciphering oracle bone script remains a formidable challenge, with only
approximately 1,600 of the over 4,500 extant characters elucidated to date.
Further scholarly investigation is required to comprehensively understand this
ancient writing system. Artificial Intelligence technology is a promising
avenue for deciphering oracle bone characters, particularly concerning their
evolution. However, one of the challenges is the lack of datasets mapping the
evolution of these characters over time. In this study, we systematically
collected ancient characters from authoritative texts and websites spanning six
historical stages: Oracle Bone Characters - OBC (15th century B.C.), Bronze
Inscriptions - BI (13th to 221 B.C.), Seal Script - SS (11th to 8th centuries
B.C.), Spring and Autumn period Characters - SAC (770 to 476 B.C.), Warring
States period Characters - WSC (475 B.C. to 221 B.C.), and Clerical Script - CS
(221 B.C. to 220 A.D.). Subsequently, we constructed an extensive dataset,
namely EVolution Oracle Bone Characters (EVOBC), consisting of 229,170 images
representing 13,714 distinct character categories. We conducted validation and
simulated deciphering on the constructed dataset, and the results demonstrate
its high efficacy in aiding the study of oracle bone script. This openly
accessible dataset aims to digitalize ancient Chinese scripts across multiple
eras, facilitating the decipherment of oracle bone script by examining the
evolution of glyph forms.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 03:30:47 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Feb 2024 08:21:50 GMT"
}
] | 1,707,868,800,000 | [
[
"Guan",
"Haisu",
""
],
[
"Wan",
"Jinpeng",
""
],
[
"Liu",
"Yuliang",
""
],
[
"Wang",
"Pengjie",
""
],
[
"Zhang",
"Kaile",
""
],
[
"Kuang",
"Zhebin",
""
],
[
"Wang",
"Xinyu",
""
],
[
"Bai",
"Xiang",
""
],
[
"Jin",
"Lianwen",
""
]
] |
2401.12557 | Xiaoxi Wang | Xiaoxi Wang | Balancing the AI Strength of Roles in Self-Play Training with Regret
Matching+ | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | When training artificial intelligence for games encompassing multiple roles,
the development of a generalized model capable of controlling any character
within the game presents a viable option. This strategy not only conserves
computational resources and time during the training phase but also reduces
resource requirements during deployment. training such a generalized model
often encounters challenges related to uneven capabilities when controlling
different roles. A simple method is introduced based on Regret Matching+, which
facilitates a more balanced performance of strength by the model when
controlling various roles.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 08:27:38 GMT"
},
{
"version": "v2",
"created": "Thu, 1 Feb 2024 03:22:22 GMT"
}
] | 1,706,832,000,000 | [
[
"Wang",
"Xiaoxi",
""
]
] |
2401.12599 | Demiao Lin | Demiao Lin (chatdoc.com) | Revolutionizing Retrieval-Augmented Generation with Enhanced PDF
Structure Recognition | 18 pages, 16 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | With the rapid development of Large Language Models (LLMs),
Retrieval-Augmented Generation (RAG) has become a predominant method in the
field of professional knowledge-based question answering. Presently, major
foundation model companies have opened up Embedding and Chat API interfaces,
and frameworks like LangChain have already integrated the RAG process. It
appears that the key models and steps in RAG have been resolved, leading to the
question: are professional knowledge QA systems now approaching perfection?
This article discovers that current primary methods depend on the premise of
accessing high-quality text corpora. However, since professional documents are
mainly stored in PDFs, the low accuracy of PDF parsing significantly impacts
the effectiveness of professional knowledge-based QA. We conducted an empirical
RAG experiment across hundreds of questions from the corresponding real-world
professional documents. The results show that, ChatDOC, a RAG system equipped
with a panoptic and pinpoint PDF parser, retrieves more accurate and complete
segments, and thus better answers. Empirical experiments show that ChatDOC is
superior to baseline on nearly 47% of questions, ties for 38% of cases, and
falls short on only 15% of cases. It shows that we may revolutionize RAG with
enhanced PDF structure recognition.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 09:54:36 GMT"
}
] | 1,706,054,400,000 | [
[
"Lin",
"Demiao",
"",
"chatdoc.com"
]
] |
2401.12666 | Rui Zhang | Hong Zhou, Rui Zhang, Peifeng Lai, Chaoran Guo, Yong Wang, Zhida Sun
and Junjie Li | EL-VIT: Probing Vision Transformer with Interactive Visualization | 10 pages, 7 figures, conference | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Nowadays, Vision Transformer (ViT) is widely utilized in various computer
vision tasks, owing to its unique self-attention mechanism. However, the model
architecture of ViT is complex and often challenging to comprehend, leading to
a steep learning curve. ViT developers and users frequently encounter
difficulties in interpreting its inner workings. Therefore, a visualization
system is needed to assist ViT users in understanding its functionality. This
paper introduces EL-VIT, an interactive visual analytics system designed to
probe the Vision Transformer and facilitate a better understanding of its
operations. The system consists of four layers of visualization views. The
first three layers include model overview, knowledge background graph, and
model detail view. These three layers elucidate the operation process of ViT
from three perspectives: the overall model architecture, detailed explanation,
and mathematical operations, enabling users to understand the underlying
principles and the transition process between layers. The fourth interpretation
view helps ViT users and experts gain a deeper understanding by calculating the
cosine similarity between patches. Our two usage scenarios demonstrate the
effectiveness and usability of EL-VIT in helping ViT users understand the
working mechanism of ViT.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 11:21:32 GMT"
}
] | 1,706,054,400,000 | [
[
"Zhou",
"Hong",
""
],
[
"Zhang",
"Rui",
""
],
[
"Lai",
"Peifeng",
""
],
[
"Guo",
"Chaoran",
""
],
[
"Wang",
"Yong",
""
],
[
"Sun",
"Zhida",
""
],
[
"Li",
"Junjie",
""
]
] |
2401.12672 | Sen Lin | Yun Peng, Sen Lin, Qian Chen, Lyu Xu, Xiaojun Ren, Yafei Li, Jianliang
Xu | ChatGraph: Chat with Your Graphs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph analysis is fundamental in real-world applications. Traditional
approaches rely on SPARQL-like languages or clicking-and-dragging interfaces to
interact with graph data. However, these methods either require users to
possess high programming skills or support only a limited range of graph
analysis functionalities. To address the limitations, we propose a large
language model (LLM)-based framework called ChatGraph. With ChatGraph, users
can interact with graphs through natural language, making it easier to use and
more flexible than traditional approaches. The core of ChatGraph lies in
generating chains of graph analysis APIs based on the understanding of the
texts and graphs inputted in the user prompts. To achieve this, ChatGraph
consists of three main modules: an API retrieval module that searches for
relevant APIs, a graph-aware LLM module that enables the LLM to comprehend
graphs, and an API chain-oriented finetuning module that guides the LLM in
generating API chains.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 11:29:19 GMT"
}
] | 1,706,054,400,000 | [
[
"Peng",
"Yun",
""
],
[
"Lin",
"Sen",
""
],
[
"Chen",
"Qian",
""
],
[
"Xu",
"Lyu",
""
],
[
"Ren",
"Xiaojun",
""
],
[
"Li",
"Yafei",
""
],
[
"Xu",
"Jianliang",
""
]
] |
2401.12700 | Chenwang Wu | Qingyang Wang, Chenwang Wu, Defu Lian, Enhong Chen | Securing Recommender System via Cooperative Training | arXiv admin note: text overlap with arXiv:2210.13762 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recommender systems are often susceptible to well-crafted fake profiles,
leading to biased recommendations. Among existing defense methods,
data-processing-based methods inevitably exclude normal samples, while
model-based methods struggle to enjoy both generalization and robustness. To
this end, we suggest integrating data processing and the robust model to
propose a general framework, Triple Cooperative Defense (TCD), which employs
three cooperative models that mutually enhance data and thereby improve
recommendation robustness. Furthermore, Considering that existing attacks
struggle to balance bi-level optimization and efficiency, we revisit poisoning
attacks in recommender systems and introduce an efficient attack strategy,
Co-training Attack (Co-Attack), which cooperatively optimizes the attack
optimization and model training, considering the bi-level setting while
maintaining attack efficiency. Moreover, we reveal a potential reason for the
insufficient threat of existing attacks is their default assumption of
optimizing attacks in undefended scenarios. This overly optimistic setting
limits the potential of attacks. Consequently, we put forth a Game-based
Co-training Attack (GCoAttack), which frames the proposed CoAttack and TCD as a
game-theoretic process, thoroughly exploring CoAttack's attack potential in the
cooperative training of attack and defense. Extensive experiments on three real
datasets demonstrate TCD's superiority in enhancing model robustness.
Additionally, we verify that the two proposed attack strategies significantly
outperform existing attacks, with game-based GCoAttack posing a greater
poisoning threat than CoAttack.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 12:07:20 GMT"
}
] | 1,706,054,400,000 | [
[
"Wang",
"Qingyang",
""
],
[
"Wu",
"Chenwang",
""
],
[
"Lian",
"Defu",
""
],
[
"Chen",
"Enhong",
""
]
] |
2401.12846 | Lior Limonad | Dirk Fahland, Fabiana Fournier, Lior Limonad, Inna Skarbovsky, Ava
J.E. Swevels | How well can large language models explain business processes? | 39 pages, 12 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) are likely to play a prominent role in future
AI-augmented business process management systems (ABPMSs) catering
functionalities across all system lifecycle stages. One such system's
functionality is Situation-Aware eXplainability (SAX), which relates to
generating causally sound and yet human-interpretable explanations that take
into account the process context in which the explained condition occurred. In
this paper, we present the SAX4BPM framework developed to generate SAX
explanations. The SAX4BPM suite consists of a set of services and a central
knowledge repository. The functionality of these services is to elicit the
various knowledge ingredients that underlie SAX explanations. A key innovative
component among these ingredients is the causal process execution view. In this
work, we integrate the framework with an LLM to leverage its power to
synthesize the various input ingredients for the sake of improved SAX
explanations. Since the use of LLMs for SAX is also accompanied by a certain
degree of doubt related to its capacity to adequately fulfill SAX along with
its tendency for hallucination and lack of inherent capacity to reason, we
pursued a methodological evaluation of the quality of the generated
explanations. To this aim, we developed a designated scale and conducted a
rigorous user study. Our findings show that the input presented to the LLMs
aided with the guard-railing of its performance, yielding SAX explanations
having better-perceived fidelity. This improvement is moderated by the
perception of trust and curiosity. More so, this improvement comes at the cost
of the perceived interpretability of the explanation.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 15:29:26 GMT"
}
] | 1,706,659,200,000 | [
[
"Fahland",
"Dirk",
""
],
[
"Fournier",
"Fabiana",
""
],
[
"Limonad",
"Lior",
""
],
[
"Skarbovsky",
"Inna",
""
],
[
"Swevels",
"Ava J. E.",
""
]
] |
2401.12869 | Zhiruo Wang | Zhiruo Wang, Daniel Fried, Graham Neubig | TroVE: Inducing Verifiable and Efficient Toolboxes for Solving
Programmatic Tasks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Language models (LMs) can solve tasks such as answering questions about
tables or images by writing programs. However, using primitive functions often
leads to verbose and error-prone programs, and higher-level functions require
expert design. To enable better solutions without human labor, we ask code LMs
to curate reusable high-level functions, and use them to write solutions. We
present TROVE, a training-free method of inducing a verifiable and efficient
toolbox of functions, by generating via using, growing, and periodically
trimming the toolbox. On 11 datasets from math, table question answering, and
image reasoning tasks, TROVE consistently yields simpler solutions with higher
accuracy than baselines using CODELLAMA and previous methods using GPT, while
using 79-98% smaller toolboxes. TROVE further enables 31% faster and 13% more
accurate human verification than baselines. With the same pipeline, it creates
diverse functions for varied tasks and datasets, providing insights into their
individual characteristics.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 16:03:17 GMT"
}
] | 1,706,054,400,000 | [
[
"Wang",
"Zhiruo",
""
],
[
"Fried",
"Daniel",
""
],
[
"Neubig",
"Graham",
""
]
] |
2401.12917 | Lancelot Da Costa | Lancelot Da Costa, Samuel Tenka, Dominic Zhao, Noor Sajid | Active Inference as a Model of Agency | Accepted in RLDM2022 for the workshop 'RL as a model of agency' | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Is there a canonical way to think of agency beyond reward maximisation? In
this paper, we show that any type of behaviour complying with physically sound
assumptions about how macroscopic biological agents interact with the world
canonically integrates exploration and exploitation in the sense of minimising
risk and ambiguity about states of the world. This description, known as active
inference, refines the free energy principle, a popular descriptive framework
for action and perception originating in neuroscience. Active inference
provides a normative Bayesian framework to simulate and model agency that is
widely used in behavioural neuroscience, reinforcement learning (RL) and
robotics. The usefulness of active inference for RL is three-fold. \emph{a})
Active inference provides a principled solution to the exploration-exploitation
dilemma that usefully simulates biological agency. \emph{b}) It provides an
explainable recipe to simulate behaviour, whence behaviour follows as an
explainable mixture of exploration and exploitation under a generative world
model, and all differences in behaviour are explicit in differences in world
model. \emph{c}) This framework is universal in the sense that it is
theoretically possible to rewrite any RL algorithm conforming to the
descriptive assumptions of active inference as an active inference algorithm.
Thus, active inference can be used as a tool to uncover and compare the
commitments and assumptions of more specific models of agency.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 17:09:25 GMT"
}
] | 1,706,054,400,000 | [
[
"Da Costa",
"Lancelot",
""
],
[
"Tenka",
"Samuel",
""
],
[
"Zhao",
"Dominic",
""
],
[
"Sajid",
"Noor",
""
]
] |
2401.12920 | Rei Tamaru | Rei Tamaru, Yang Cheng, Steven Parker, Ernie Perry, Bin Ran, Soyoung
Ahn | Truck Parking Usage Prediction with Decomposed Graph Neural Networks | 10 pages, 5 figures, 3 tables, Manuscript for IEEE Transactions on
Intelligent Transportation Systems | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Truck parking on freight corridors faces various challenges, such as
insufficient parking spaces and compliance with Hour-of-Service (HOS)
regulations. These constraints often result in unauthorized parking practices,
causing safety concerns. To enhance the safety of freight operations, providing
accurate parking usage prediction proves to be a cost-effective solution.
Despite the existing research demonstrating satisfactory accuracy for
predicting individual truck parking site usage, few approaches have been
proposed for predicting usage with spatial dependencies of multiple truck
parking sites. We present the Regional Temporal Graph Neural Network (RegT-GCN)
as a predictive framework for assessing parking usage across the entire state
to provide better truck parking information and mitigate unauthorized parking.
The framework leverages the topological structures of truck parking site
distributions and historical parking data to predict occupancy rates across a
state. To achieve this, we introduce a Regional Decomposition approach, which
effectively captures the geographical characteristics. We also introduce the
spatial module working efficiently with the temporal module. Evaluation results
demonstrate that the proposed model surpasses other baseline models, improving
the performance by more than $20\%$ compared with the original model. The
proposed model allows truck parking sites' percipience of the topological
structures and provides higher performance.
| [
{
"version": "v1",
"created": "Tue, 23 Jan 2024 17:14:01 GMT"
}
] | 1,706,054,400,000 | [
[
"Tamaru",
"Rei",
""
],
[
"Cheng",
"Yang",
""
],
[
"Parker",
"Steven",
""
],
[
"Perry",
"Ernie",
""
],
[
"Ran",
"Bin",
""
],
[
"Ahn",
"Soyoung",
""
]
] |
2401.13752 | Hana Chockler | Hana Chockler and Joseph Y. Halpern | Explaining Image Classifiers | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We focus on explaining image classifiers, taking the work of Mothilal et al.
[2021] (MMTS) as our point of departure. We observe that, although MMTS claim
to be using the definition of explanation proposed by Halpern [2016], they do
not quite do so. Roughly speaking, Halpern's definition has a necessity clause
and a sufficiency clause. MMTS replace the necessity clause by a requirement
that, as we show, implies it. Halpern's definition also allows agents to
restrict the set of options considered. While these difference may seem minor,
as we show, they can have a nontrivial impact on explanations. We also show
that, essentially without change, Halpern's definition can handle two issues
that have proved difficult for other approaches: explanations of absence (when,
for example, an image classifier for tumors outputs "no tumor") and
explanations of rare events (such as tumors).
| [
{
"version": "v1",
"created": "Wed, 24 Jan 2024 19:12:38 GMT"
}
] | 1,706,227,200,000 | [
[
"Chockler",
"Hana",
""
],
[
"Halpern",
"Joseph Y.",
""
]
] |
2401.13883 | Ryo Kuroiwa | Ryo Kuroiwa, J. Christopher Beck | Domain-Independent Dynamic Programming | Manuscript submitted to Artificial Intelligence | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | For combinatorial optimization problems, model-based paradigms such as
mixed-integer programming (MIP) and constraint programming (CP) aim to decouple
modeling and solving a problem: the `holy grail' of declarative problem
solving. We propose domain-independent dynamic programming (DIDP), a new
model-based paradigm based on dynamic programming (DP). While DP is not new, it
has typically been implemented as a problem-specific method. We introduce
Dynamic Programming Description Language (DyPDL), a formalism to define DP
models based on a state transition system, inspired by AI planning. We show
that heuristic search algorithms can be used to solve DyPDL models and propose
seven DIDP solvers. We experimentally compare our DIDP solvers with commercial
MIP and CP solvers (solving MIP and CP models, respectively) on common
benchmark instances of eleven combinatorial optimization problem classes. We
show that DIDP outperforms MIP in nine problem classes, CP also in nine problem
classes, and both MIP and CP in seven.
| [
{
"version": "v1",
"created": "Thu, 25 Jan 2024 01:48:09 GMT"
},
{
"version": "v2",
"created": "Fri, 31 May 2024 21:05:34 GMT"
}
] | 1,717,459,200,000 | [
[
"Kuroiwa",
"Ryo",
""
],
[
"Beck",
"J. Christopher",
""
]
] |
2401.14153 | Javier Carbo | J. Carbo, N. Sanchez, J. M. Molina | Agent-based Simulation with Netlogo to Evaluate AmI Scenarios | null | null | 10.1057/jos.2016.10 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper an agent-based simulation is developed in order to evaluate an
AmI scenario based on agents. Many AmI applications are implemented through
agents but they are not compared to any other existing alternative in order to
evaluate the relative benefits of using them. The proposal simulation
environment developed in Netlogo analyse such benefits using two evaluation
criteria: First, measuring agent satisfaction of different types of desires
along the execution. Second, measuring time savings obtained through a correct
use of context information.
So, here, a previously suggested agent architecture, an ontology and a
12-steps protocol to provide AmI services in airports, is evaluated using a
NetLogo simulation environment. The present work uses a NetLogo model
considering scalability problems of this application domain but using FIPA and
BDI extensions to be coherent with our previous works and our previous JADE
implementation of them.
The NetLogo model presented simulates an airport with agent users passing
through several zones located in a specific order in a map: passport controls,
check-in counters of airline companies, boarding gates, different types of
shopping. Although initial data in simulations are generated randomly, and the
model is just an approximation of real-world airports, the definition of this
case of use of Ambient Intelligence through NetLogo agents opens an interesting
way to evaluate the benefits of using Ambient Intelligence, which is a
significant contribution to the final development of them.
| [
{
"version": "v1",
"created": "Thu, 25 Jan 2024 13:05:06 GMT"
}
] | 1,706,227,200,000 | [
[
"Carbo",
"J.",
""
],
[
"Sanchez",
"N.",
""
],
[
"Molina",
"J. M.",
""
]
] |
2401.14511 | Sascha Ossowski | Joaqu\'in Arias, Mar Moreno-Rebato, Jos\'e A. Rodr\'iguez-Garc\'ia,
Sascha Ossowski | Automated legal reasoning with discretion to act using s(LAW) | null | Artificial Intelligence and Law (2023) | 10.1007/s10506-023-09376-5 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Automated legal reasoning and its application in smart contracts and
automated decisions are increasingly attracting interest. In this context,
ethical and legal concerns make it necessary for automated reasoners to justify
in human-understandable terms the advice given. Logic Programming, specially
Answer Set Programming, has a rich semantics and has been used to very
concisely express complex knowledge. However, modelling discretionality to act
and other vague concepts such as ambiguity cannot be expressed in top-down
execution models based on Prolog, and in bottom-up execution models based on
ASP the justifications are incomplete and/or not scalable. We propose to use
s(CASP), a top-down execution model for predicate ASP, to model vague concepts
following a set of patterns. We have implemented a framework, called s(LAW), to
model, reason, and justify the applicable legislation and validate it by
translating (and benchmarking) a representative use case, the criteria for the
admission of students in the "Comunidad de Madrid".
| [
{
"version": "v1",
"created": "Thu, 25 Jan 2024 21:11:08 GMT"
}
] | 1,706,486,400,000 | [
[
"Arias",
"Joaquín",
""
],
[
"Moreno-Rebato",
"Mar",
""
],
[
"Rodríguez-García",
"José A.",
""
],
[
"Ossowski",
"Sascha",
""
]
] |
2401.14636 | Felipe Trevizan | Johannes Schmalz, Felipe Trevizan | Efficient Constraint Generation for Stochastic Shortest Path Problems | Extended version of AAAI 2024 paper | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Current methods for solving Stochastic Shortest Path Problems (SSPs) find
states' costs-to-go by applying Bellman backups, where state-of-the-art methods
employ heuristics to select states to back up and prune. A fundamental
limitation of these algorithms is their need to compute the cost-to-go for
every applicable action during each state backup, leading to unnecessary
computation for actions identified as sub-optimal. We present new connections
between planning and operations research and, using this framework, we address
this issue of unnecessary computation by introducing an efficient version of
constraint generation for SSPs. This technique allows algorithms to ignore
sub-optimal actions and avoid computing their costs-to-go. We also apply our
novel technique to iLAO* resulting in a new algorithm, CG-iLAO*. Our
experiments show that CG-iLAO* ignores up to 57% of iLAO*'s actions and it
solves problems up to 8x and 3x faster than LRTDP and iLAO*.
| [
{
"version": "v1",
"created": "Fri, 26 Jan 2024 04:00:07 GMT"
}
] | 1,706,486,400,000 | [
[
"Schmalz",
"Johannes",
""
],
[
"Trevizan",
"Felipe",
""
]
] |
2401.14743 | Takanori Ugai | Takanori Ugai, Shusaku Egami, Swe Nwe Nwe Htun, Kouji Kozaki, Takahiro
Kawamura, Ken Fukuda | Synthetic Multimodal Dataset for Empowering Safety and Well-being in
Home Environments | 7 pages, 2 figures,4 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presents a synthetic multimodal dataset of daily activities that
fuses video data from a 3D virtual space simulator with knowledge graphs
depicting the spatiotemporal context of the activities. The dataset is
developed for the Knowledge Graph Reasoning Challenge for Social Issues
(KGRC4SI), which focuses on identifying and addressing hazardous situations in
the home environment. The dataset is available to the public as a valuable
resource for researchers and practitioners developing innovative solutions
recognizing human behaviors to enhance safety and well-being in
| [
{
"version": "v1",
"created": "Fri, 26 Jan 2024 10:05:41 GMT"
}
] | 1,706,486,400,000 | [
[
"Ugai",
"Takanori",
""
],
[
"Egami",
"Shusaku",
""
],
[
"Htun",
"Swe Nwe Nwe",
""
],
[
"Kozaki",
"Kouji",
""
],
[
"Kawamura",
"Takahiro",
""
],
[
"Fukuda",
"Ken",
""
]
] |
2401.14933 | Idoia Berges | Idoia Berges, Jes\'us Berm\'udez, Arantza Illarramendi | SSDOnt: an Ontology for representing Single-Subject Design Studies | This document is the Accepted Manuscript version of a Published Work
that appeared in final form in Methods of Information in Medicine 57(01/02) :
55-61 (2018), copyright 2018 Schattauer. To access the final edited and
published work see https://doi.org/10.3414/ME17-01-0109 | Methods of Information in Medicine 57(01/02) : 55-61 (2018) | 10.3414/ME17-01-0109 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Single-Subject Design is used in several areas such as education
and biomedicine. However, no suited formal vocabulary exists for annotating the
detailed configuration and the results of this type of research studies with
the appropriate granularity for looking for information about them. Therefore,
the search for those study designs relies heavily on a syntactical search on
the abstract, keywords or full text of the publications about the study, which
entails some limitations. Objective: To present SSDOnt, a specific purpose
ontology for describing and annotating single-subject design studies, so that
complex questions can be asked about them afterwards. Methods: The ontology was
developed following the NeOn methodology. Once the requirements of the ontology
were defined, a formal model was described in a Description Logic and later
implemented in the ontology language OWL 2 DL. Results: We show how the
ontology provides a reference model with a suitable terminology for the
annotation and searching of single-subject design studies and their main
components, such as the phases, the intervention types, the outcomes and the
results. Some mappings with terms of related ontologies have been established.
We show as proof-of-concept that classes in the ontology can be easily extended
to annotate more precise information about specific interventions and outcomes
such as those related to autism. Moreover, we provide examples of some types of
queries that can be posed to the ontology. Conclusions: SSDOnt has achieved the
purpose of covering the descriptions of the domain of single-subject research
studies.
| [
{
"version": "v1",
"created": "Fri, 26 Jan 2024 15:11:31 GMT"
}
] | 1,706,486,400,000 | [
[
"Berges",
"Idoia",
""
],
[
"Bermúdez",
"Jesús",
""
],
[
"Illarramendi",
"Arantza",
""
]
] |
2401.15188 | Yixue Zhao | Sheng Yu, Narjes Nourzad, Randye J. Semple, Yixue Zhao, Emily Zhou,
Bhaskar Krishnamachari | CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for
Mental Health | MOBILESoft 2024 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The COVID-19 pandemic has intensified the urgency for effective and
accessible mental health interventions in people's daily lives. Mobile Health
(mHealth) solutions, such as AI Chatbots and Mindfulness Apps, have gained
traction as they expand beyond traditional clinical settings to support daily
life. However, the effectiveness of current mHealth solutions is impeded by the
lack of context-awareness, personalization, and modularity to foster their
reusability. This paper introduces CAREForMe, a contextual multi-armed bandit
(CMAB) recommendation framework for mental health. Designed with
context-awareness, personalization, and modularity at its core, CAREForMe
harnesses mobile sensing and integrates online learning algorithms with user
clustering capability to deliver timely, personalized recommendations. With its
modular design, CAREForMe serves as both a customizable recommendation
framework to guide future research, and a collaborative platform to facilitate
interdisciplinary contributions in mHealth research. We showcase CAREForMe's
versatility through its implementation across various platforms (e.g., Discord,
Telegram) and its customization to diverse recommendation features.
| [
{
"version": "v1",
"created": "Fri, 26 Jan 2024 20:18:25 GMT"
}
] | 1,706,572,800,000 | [
[
"Yu",
"Sheng",
""
],
[
"Nourzad",
"Narjes",
""
],
[
"Semple",
"Randye J.",
""
],
[
"Zhao",
"Yixue",
""
],
[
"Zhou",
"Emily",
""
],
[
"Krishnamachari",
"Bhaskar",
""
]
] |
2401.15196 | Jiachen Xi | Jiachen Xi, Alfredo Garcia, Petar Momcilovic | Regularized Q-Learning with Linear Function Approximation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several successful reinforcement learning algorithms make use of
regularization to promote multi-modal policies that exhibit enhanced
exploration and robustness. With functional approximation, the convergence
properties of some of these algorithms (e.g. soft Q-learning) are not well
understood. In this paper, we consider a single-loop algorithm for minimizing
the projected Bellman error with finite time convergence guarantees in the case
of linear function approximation. The algorithm operates on two scales: a
slower scale for updating the target network of the state-action values, and a
faster scale for approximating the Bellman backups in the subspace of the span
of basis vectors. We show that, under certain assumptions, the proposed
algorithm converges to a stationary point in the presence of Markovian noise.
In addition, we provide a performance guarantee for the policies derived from
the proposed algorithm.
| [
{
"version": "v1",
"created": "Fri, 26 Jan 2024 20:45:40 GMT"
}
] | 1,706,572,800,000 | [
[
"Xi",
"Jiachen",
""
],
[
"Garcia",
"Alfredo",
""
],
[
"Momcilovic",
"Petar",
""
]
] |
2401.15443 | Zibin Dong | Zibin Dong, Jianye Hao, Yifu Yuan, Fei Ni, Yitian Wang, Pengyi Li and
Yan Zheng | DiffuserLite: Towards Real-time Diffusion Planning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Diffusion planning has been recognized as an effective decision-making
paradigm in various domains. The capability of conditionally generating
high-quality long-horizon trajectories makes it a promising research direction.
However, existing diffusion planning methods suffer from low decision-making
frequencies due to the expensive iterative sampling cost. To address this
issue, we introduce DiffuserLite, a super fast and lightweight diffusion
planning framework. DiffuserLite employs a planning refinement process (PRP) to
generate coarse-to-fine-grained trajectories, significantly reducing the
modeling of redundant information and leading to notable increases in
decision-making frequency. Our experimental results demonstrate that
DiffuserLite achieves a decision-making frequency of $122$Hz ($112.7$x faster
than previous mainstream frameworks) and reaches state-of-the-art performance
on D4RL benchmarks. In addition, our neat DiffuserLite framework can serve as a
flexible plugin to enhance decision frequency in other diffusion planning
algorithms, providing a structural design reference for future works. More
details and visualizations are available at https://diffuserlite.github.io/.
| [
{
"version": "v1",
"created": "Sat, 27 Jan 2024 15:30:49 GMT"
},
{
"version": "v2",
"created": "Tue, 30 Jan 2024 04:43:27 GMT"
},
{
"version": "v3",
"created": "Wed, 31 Jan 2024 02:50:41 GMT"
},
{
"version": "v4",
"created": "Fri, 2 Feb 2024 08:57:16 GMT"
}
] | 1,707,091,200,000 | [
[
"Dong",
"Zibin",
""
],
[
"Hao",
"Jianye",
""
],
[
"Yuan",
"Yifu",
""
],
[
"Ni",
"Fei",
""
],
[
"Wang",
"Yitian",
""
],
[
"Li",
"Pengyi",
""
],
[
"Zheng",
"Yan",
""
]
] |
2401.15621 | Sergey Zeltyn Dr. | Alon Oved, Segev Shlomov, Sergey Zeltyn, Nir Mashkif and Avi Yaeli | SNAP: Semantic Stories for Next Activity Prediction | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting the next activity in an ongoing process is one of the most common
classification tasks in the business process management (BPM) domain. It allows
businesses to optimize resource allocation, enhance operational efficiency, and
aids in risk mitigation and strategic decision-making. This provides a
competitive edge in the rapidly evolving confluence of BPM and AI. Existing
state-of-the-art AI models for business process prediction do not fully
capitalize on available semantic information within process event logs. As
current advanced AI-BPM systems provide semantically-richer textual data, the
need for novel adequate models grows. To address this gap, we propose the novel
SNAP method that leverages language foundation models by constructing semantic
contextual stories from the process historical event logs and using them for
the next activity prediction. We compared the SNAP algorithm with nine
state-of-the-art models on six benchmark datasets and show that SNAP
significantly outperforms them, especially for datasets with high levels of
semantic content.
| [
{
"version": "v1",
"created": "Sun, 28 Jan 2024 10:20:15 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Mar 2024 17:22:37 GMT"
}
] | 1,710,460,800,000 | [
[
"Oved",
"Alon",
""
],
[
"Shlomov",
"Segev",
""
],
[
"Zeltyn",
"Sergey",
""
],
[
"Mashkif",
"Nir",
""
],
[
"Yaeli",
"Avi",
""
]
] |
2401.16045 | Lingning Song | Lingning Song and Yi Zu and Shan Lu and Jieyue He | Type-based Neural Link Prediction Adapter for Complex Query Answering | 11 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Answering complex logical queries on incomplete knowledge graphs (KGs) is a
fundamental and challenging task in multi-hop reasoning. Recent work defines
this task as an end-to-end optimization problem, which significantly reduces
the training cost and enhances the generalization of the model by a pretrained
link predictors for query answering. However, most existing proposals ignore
the critical semantic knowledge inherently available in KGs, such as type
information, which could help answer complex logical queries. To this end, we
propose TypE-based Neural Link Prediction Adapter (TENLPA), a novel model that
constructs type-based entity-relation graphs to discover the latent
relationships between entities and relations by leveraging type information in
KGs. Meanwhile, in order to effectively combine type information with complex
logical queries, an adaptive learning mechanism is introduced, which is trained
by back-propagating during the complex query answering process to achieve
adaptive adjustment of neural link predictors. Experiments on 3 standard
datasets show that TENLPA model achieves state-of-the-art performance on
complex query answering with good generalization and robustness.
| [
{
"version": "v1",
"created": "Mon, 29 Jan 2024 10:54:28 GMT"
}
] | 1,706,572,800,000 | [
[
"Song",
"Lingning",
""
],
[
"Zu",
"Yi",
""
],
[
"Lu",
"Shan",
""
],
[
"He",
"Jieyue",
""
]
] |
2401.16119 | Xuefeng Liang | Ying Zhou, Xuefeng Liang, Han Chen, Yin Zhao, Xin Chen, Lida Yu | Triple Disentangled Representation Learning for Multimodal Affective
Analysis | 14 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal learning has exhibited a significant advantage in affective
analysis tasks owing to the comprehensive information of various modalities,
particularly the complementary information. Thus, many emerging studies focus
on disentangling the modality-invariant and modality-specific representations
from input data and then fusing them for prediction. However, our study shows
that modality-specific representations may contain information that is
irrelevant or conflicting with the tasks, which downgrades the effectiveness of
learned multimodal representations. We revisit the disentanglement issue, and
propose a novel triple disentanglement approach, TriDiRA, which disentangles
the modality-invariant, effective modality-specific and ineffective
modality-specific representations from input data. By fusing only the
modality-invariant and effective modality-specific representations, TriDiRA can
significantly alleviate the impact of irrelevant and conflicting information
across modalities during model training. Extensive experiments conducted on
four benchmark datasets demonstrate the effectiveness and generalization of our
triple disentanglement, which outperforms SOTA methods.
| [
{
"version": "v1",
"created": "Mon, 29 Jan 2024 12:45:27 GMT"
},
{
"version": "v2",
"created": "Mon, 8 Apr 2024 08:19:19 GMT"
}
] | 1,712,620,800,000 | [
[
"Zhou",
"Ying",
""
],
[
"Liang",
"Xuefeng",
""
],
[
"Chen",
"Han",
""
],
[
"Zhao",
"Yin",
""
],
[
"Chen",
"Xin",
""
],
[
"Yu",
"Lida",
""
]
] |
2401.16124 | Klaus Strauch | Javier Romero, Torsten Schaub, Klaus Strauch | On the generalization of learned constraints for ASP solving in temporal
domains | 28 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The representation of a dynamic problem in ASP usually boils down to using
copies of variables and constraints, one for each time stamp, no matter whether
it is directly encoded or via an action or temporal language. The
multiplication of variables and constraints is commonly done during grounding
and the solver is completely ignorant about the temporal relationship among the
different instances. On the other hand, a key factor in the performance of
today's ASP solvers is conflict-driven constraint learning. Our question is now
whether a constraint learned for particular time steps can be generalized and
reused at other time stamps, and ultimately whether this enhances the overall
solver performance on temporal problems. Knowing full well the domain of time,
we study conditions under which learned dynamic constraints can be generalized.
We propose a simple translation of the original logic program such that, for
the translated programs, the learned constraints can be generalized to other
time points. Additionally, we identify a property of temporal problems that
allows us to generalize all learned constraints to all time steps. It turns out
that this property is satisfied by many planning problems. Finally, we
empirically evaluate the impact of adding the generalized constraints to an ASP
solver
| [
{
"version": "v1",
"created": "Mon, 29 Jan 2024 12:49:09 GMT"
}
] | 1,706,572,800,000 | [
[
"Romero",
"Javier",
""
],
[
"Schaub",
"Torsten",
""
],
[
"Strauch",
"Klaus",
""
]
] |
2401.16270 | Steven Schockaert | Victor Charpenay, Steven Schockaert | Capturing Knowledge Graphs and Rules with Octagon Embeddings | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Region based knowledge graph embeddings represent relations as geometric
regions. This has the advantage that the rules which are captured by the model
are made explicit, making it straightforward to incorporate prior knowledge and
to inspect learned models. Unfortunately, existing approaches are severely
restricted in their ability to model relational composition, and hence also
their ability to model rules, thus failing to deliver on the main promise of
region based models. With the aim of addressing these limitations, we
investigate regions which are composed of axis-aligned octagons. Such octagons
are particularly easy to work with, as intersections and compositions can be
straightforwardly computed, while they are still sufficiently expressive to
model arbitrary knowledge graphs. Among others, we also show that our octagon
embeddings can properly capture a non-trivial class of rule bases. Finally, we
show that our model achieves competitive experimental results.
| [
{
"version": "v1",
"created": "Mon, 29 Jan 2024 16:18:54 GMT"
}
] | 1,706,572,800,000 | [
[
"Charpenay",
"Victor",
""
],
[
"Schockaert",
"Steven",
""
]
] |
2401.16398 | Federico Malato | Federco Malato, Florian Leopold, Andrew Melnik, Ville Hautamaki | Zero-shot Imitation Policy via Search in Demonstration Dataset | null | null | 10.1109/ICASSP48485.2024.10447339 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Behavioral cloning uses a dataset of demonstrations to learn a policy. To
overcome computationally expensive training procedures and address the policy
adaptation problem, we propose to use latent spaces of pre-trained foundation
models to index a demonstration dataset, instantly access similar relevant
experiences, and copy behavior from these situations. Actions from a selected
similar situation can be performed by the agent until representations of the
agent's current situation and the selected experience diverge in the latent
space. Thus, we formulate our control problem as a dynamic search problem over
a dataset of experts' demonstrations. We test our approach on BASALT
MineRL-dataset in the latent representation of a Video Pre-Training model. We
compare our model to state-of-the-art, Imitation Learning-based Minecraft
agents. Our approach can effectively recover meaningful demonstrations and show
human-like behavior of an agent in the Minecraft environment in a wide variety
of scenarios. Experimental results reveal that performance of our search-based
approach clearly wins in terms of accuracy and perceptual evaluation over
learning-based models.
| [
{
"version": "v1",
"created": "Mon, 29 Jan 2024 18:38:29 GMT"
}
] | 1,712,620,800,000 | [
[
"Malato",
"Federco",
""
],
[
"Leopold",
"Florian",
""
],
[
"Melnik",
"Andrew",
""
],
[
"Hautamaki",
"Ville",
""
]
] |
2401.16580 | Jaejin Lee | Jaejin Lee, Seho Kee, Mani Janakiram and George Runger | Attention-based Reinforcement Learning for Combinatorial Optimization:
Application to Job Shop Scheduling Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Job shop scheduling problems represent a significant and complex facet of
combinatorial optimization problems, which have traditionally been addressed
through either exact or approximate solution methodologies. However, the
practical application of these solutions is often challenged due to the
complexity of real-world problems. Even when utilizing an approximate solution
approach, the time required to identify a near-optimal solution can be
prohibitively extensive, and the solutions derived are generally not applicable
to new problems. This study proposes an innovative attention-based
reinforcement learning method specifically designed for the category of job
shop scheduling problems. This method integrates a policy gradient
reinforcement learning approach with a modified transformer architecture. A key
finding of this research is the ability of our trained learners within the
proposed method to be repurposed for larger-scale problems that were not part
of the initial training set. Furthermore, empirical evidence demonstrates that
our approach surpasses the results of recent studies and outperforms commonly
implemented heuristic rules. This suggests that our method offers a promising
avenue for future research and practical application in the field of job shop
scheduling problems.
| [
{
"version": "v1",
"created": "Mon, 29 Jan 2024 21:31:54 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Mar 2024 17:57:22 GMT"
}
] | 1,710,806,400,000 | [
[
"Lee",
"Jaejin",
""
],
[
"Kee",
"Seho",
""
],
[
"Janakiram",
"Mani",
""
],
[
"Runger",
"George",
""
]
] |
2401.17436 | Paolo Burelli | Jeppe Theiss Kristensen, Paolo Burelli | Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on
Different Methods to Combine Player Analytics and Simulated Data | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Difficulty is one of the key drivers of player engagement and it is often one
of the aspects that designers tweak most to optimise the player experience;
operationalising it is, therefore, a crucial task for game development studios.
A common practice consists of creating metrics out of data collected by player
interactions with the content; however, this allows for estimation only after
the content is released and does not consider the characteristics of potential
future players.
In this article, we present a number of potential solutions for the
estimation of difficulty under such conditions, and we showcase the results of
a comparative study intended to understand which method and which types of data
perform better in different scenarios.
The results reveal that models trained on a combination of cohort statistics
and simulated data produce the most accurate estimations of difficulty in all
scenarios. Furthermore, among these models, artificial neural networks show the
most consistent results.
| [
{
"version": "v1",
"created": "Tue, 30 Jan 2024 20:51:42 GMT"
}
] | 1,706,745,600,000 | [
[
"Kristensen",
"Jeppe Theiss",
""
],
[
"Burelli",
"Paolo",
""
]
] |
2401.17527 | Haotian Ling | Haotian Ling, Zhihai Wang, Jie Wang | Learning to Stop Cut Generation for Efficient Mixed-Integer Linear
Programming | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cutting planes (cuts) play an important role in solving mixed-integer linear
programs (MILPs), as they significantly tighten the dual bounds and improve the
solving performance. A key problem for cuts is when to stop cuts generation,
which is important for the efficiency of solving MILPs. However, many modern
MILP solvers employ hard-coded heuristics to tackle this problem, which tends
to neglect underlying patterns among MILPs from certain applications. To
address this challenge, we formulate the cuts generation stopping problem as a
reinforcement learning problem and propose a novel hybrid graph representation
model (HYGRO) to learn effective stopping strategies. An appealing feature of
HYGRO is that it can effectively capture both the dynamic and static features
of MILPs, enabling dynamic decision-making for the stopping strategies. To the
best of our knowledge, HYGRO is the first data-driven method to tackle the cuts
generation stopping problem. By integrating our approach with modern solvers,
experiments demonstrate that HYGRO significantly improves the efficiency of
solving MILPs compared to competitive baselines, achieving up to 31%
improvement.
| [
{
"version": "v1",
"created": "Wed, 31 Jan 2024 01:09:40 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Feb 2024 05:54:58 GMT"
}
] | 1,707,091,200,000 | [
[
"Ling",
"Haotian",
""
],
[
"Wang",
"Zhihai",
""
],
[
"Wang",
"Jie",
""
]
] |
2401.17710 | Pakizar Shamoi Dr | Ayana Adilova and Pakizar Shamoi | Aesthetic Preference Prediction in Interior Design: Fuzzy Approach | Submitted to IEEE conference for consideration | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interior design is all about creating spaces that look and feel good.
However, the subjective nature of aesthetic preferences presents a significant
challenge in defining and quantifying what makes an interior design visually
appealing. The current paper addresses this gap by introducing a novel
methodology for quantifying and predicting aesthetic preferences in interior
design. Our study combines fuzzy logic with image processing techniques. We
collected a dataset of interior design images from social media platforms,
focusing on essential visual attributes such as color harmony, lightness, and
complexity. We integrate these features using weighted average to compute a
general aesthetic score. Our approach considers individual color preferences in
calculating the overall aesthetic preference. We initially gather user ratings
for primary colors like red, brown, and others to understand their preferences.
Then, we use the pixel count of the top five dominant colors in the image to
get the color scheme preference. The color scheme preference and the aesthetic
score are then passed as inputs to the fuzzy inference system to calculate an
overall preference score. This score represents a comprehensive measure of the
user's preference for a particular interior design, considering their color
choices and general aesthetic appeal. We used the 2AFC (Two-Alternative Forced
Choice) method to validate our methodology, achieving a notable hit rate of
0.7. This study can help designers and professionals better understand and meet
people's interior design preferences, especially in a world that relies heavily
on digital media.
| [
{
"version": "v1",
"created": "Wed, 31 Jan 2024 09:59:59 GMT"
}
] | 1,706,745,600,000 | [
[
"Adilova",
"Ayana",
""
],
[
"Shamoi",
"Pakizar",
""
]
] |
2401.17749 | Xiao Shao | Xiao Shao, Weifu Jiang, Fei Zuo, Mengqing Liu | SwarmBrain: Embodied agent for real-time strategy game StarCraft II via
large language models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs) have recently garnered significant
accomplishments in various exploratory tasks, even surpassing the performance
of traditional reinforcement learning-based methods that have historically
dominated the agent-based field. The purpose of this paper is to investigate
the efficacy of LLMs in executing real-time strategy war tasks within the
StarCraft II gaming environment. In this paper, we introduce SwarmBrain, an
embodied agent leveraging LLM for real-time strategy implementation in the
StarCraft II game environment. The SwarmBrain comprises two key components: 1)
a Overmind Intelligence Matrix, powered by state-of-the-art LLMs, is designed
to orchestrate macro-level strategies from a high-level perspective. This
matrix emulates the overarching consciousness of the Zerg intelligence brain,
synthesizing strategic foresight with the aim of allocating resources,
directing expansion, and coordinating multi-pronged assaults. 2) a Swarm
ReflexNet, which is agile counterpart to the calculated deliberation of the
Overmind Intelligence Matrix. Due to the inherent latency in LLM reasoning, the
Swarm ReflexNet employs a condition-response state machine framework, enabling
expedited tactical responses for fundamental Zerg unit maneuvers. In the
experimental setup, SwarmBrain is in control of the Zerg race in confrontation
with an Computer-controlled Terran adversary. Experimental results show the
capacity of SwarmBrain to conduct economic augmentation, territorial expansion,
and tactical formulation, and it shows the SwarmBrain is capable of achieving
victory against Computer players set at different difficulty levels.
| [
{
"version": "v1",
"created": "Wed, 31 Jan 2024 11:14:29 GMT"
}
] | 1,706,745,600,000 | [
[
"Shao",
"Xiao",
""
],
[
"Jiang",
"Weifu",
""
],
[
"Zuo",
"Fei",
""
],
[
"Liu",
"Mengqing",
""
]
] |
2401.17783 | Mar\'ia Asunci\'on Padilla Rasc\'on | M.A. Padilla-Rascon, P. Gonzalez, C.J. Carmona | SDRDPy: An application to graphically visualize the knowledge obtained
with supervised descriptive rule algorithms | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | SDRDPy is a desktop application that allows experts an intuitive graphic and
tabular representation of the knowledge extracted by any supervised descriptive
rule discovery algorithm. The application is able to provide an analysis of the
data showing the relevant information of the data set and the relationship
between the rules, data and the quality measures associated for each rule
regardless of the tool where algorithm has been executed. All of the
information is presented in a user-friendly application in order to facilitate
expert analysis and also the exportation of reports in different formats.
| [
{
"version": "v1",
"created": "Wed, 31 Jan 2024 12:26:59 GMT"
}
] | 1,706,745,600,000 | [
[
"Padilla-Rascon",
"M. A.",
""
],
[
"Gonzalez",
"P.",
""
],
[
"Carmona",
"C. J.",
""
]
] |
2402.00048 | Bruno Sartini | Bruno Sartini | IICONGRAPH: improved Iconographic and Iconological Statements in
Knowledge Graphs | 18 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Iconography and iconology are fundamental domains when it comes to
understanding artifacts of cultural heritage. Iconography deals with the study
and interpretation of visual elements depicted in artifacts and their
symbolism, while iconology delves deeper, exploring the underlying cultural and
historical meanings. Despite the advances in representing cultural heritage
with Linked Open Data (LOD), recent studies show persistent gaps in the
representation of iconographic and iconological statements in current knowledge
graphs (KGs). To address them, this paper presents IICONGRAPH, a KG that was
created by refining and extending the iconographic and iconological statements
of ArCo (the Italian KG of cultural heritage) and Wikidata. The development of
IICONGRAPH was also driven by a series of requirements emerging from research
case studies that were unattainable in the non-reengineered versions of the
KGs. The evaluation results demonstrate that IICONGRAPH not only outperforms
ArCo and Wikidata through domain-specific assessments from the literature but
also serves as a robust platform for addressing the formulated research
questions. IICONGRAPH is released and documented in accordance with the FAIR
principles to guarantee the resource's reusability. The algorithms used to
create it and assess the research questions have also been made available to
ensure transparency and reproducibility. While future work focuses on ingesting
more data into the KG, and on implementing it as a backbone of LLM-based
question answering systems, the current version of IICONGRAPH still emerges as
a valuable asset, contributing to the evolving landscape of cultural heritage
representation within Knowledge Graphs, the Semantic Web, and beyond.
| [
{
"version": "v1",
"created": "Wed, 24 Jan 2024 15:44:16 GMT"
}
] | 1,706,832,000,000 | [
[
"Sartini",
"Bruno",
""
]
] |
2402.00064 | Javier Carbo | Javier Carbo, Jose M Molina, Miguel A Patricio | Merging plans with incomplete knowledge about actions and goals through
an agent-based reputation system | null | null | 10.1016/j.eswa.2018.07.062 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Managing transition plans is one of the major problems of people with
cognitive disabilities. Therefore, finding an automated way to generate such
plans would be a helpful tool for this community. In this paper we have
specifically proposed and compared different alternative ways to merge plans
formed by sequences of actions of unknown similarities between goals and
actions executed by several operator agents which cooperate between them
applying such actions over some passive elements (node agents) that require
additional executions of another plan after some time of use. Such ignorance of
the similarities between plan actions and goals would justify the use of a
distributed recommendation system that would provide an useful plan to be
applied for a certain goal to a given operator agent, generated from the known
results of previous executions of different plans by other operator agents.
Here we provide the general framework of execution (agent system), and the
different merging algorithms applied to this problem. The proposed agent system
would act as an useful cognitive assistant for people with intelectual
disabilities such as autism.
| [
{
"version": "v1",
"created": "Mon, 29 Jan 2024 11:34:59 GMT"
}
] | 1,706,832,000,000 | [
[
"Carbo",
"Javier",
""
],
[
"Molina",
"Jose M",
""
],
[
"Patricio",
"Miguel A",
""
]
] |
2402.00076 | Daniel Karapetyan Dr | Sahil Patel and Daniel Karapetyan | Exploitation Strategies in Conditional Markov Chain Search: A case study
on the three-index assignment problem | 14 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Conditional Markov Chain Search (CMCS) is a framework for automated
design of metaheuristics for discrete combinatorial optimisation problems.
Given a set of algorithmic components such as hill climbers and mutations, CMCS
decides in which order to apply those components. The decisions are dictated by
the CMCS configuration that can be learnt offline. CMCS does not have an
acceptance criterion; any moves are accepted by the framework. As a result, it
is particularly good in exploration but is not as good at exploitation. In this
study, we explore several extensions of the framework to improve its
exploitation abilities. To perform a computational study, we applied the
framework to the three-index assignment problem. The results of our experiments
showed that a two-stage CMCS is indeed superior to a single-stage CMCS.
| [
{
"version": "v1",
"created": "Tue, 30 Jan 2024 22:13:46 GMT"
}
] | 1,706,832,000,000 | [
[
"Patel",
"Sahil",
""
],
[
"Karapetyan",
"Daniel",
""
]
] |
2402.00083 | Kenya Andrews | Kenya Andrews and Mesrob Ohannessian and Tanya Berger-Wolf | Modeling Access Differences to Reduce Disparity in Resource Allocation | Association for Computing Machinery (2022) | null | 10.1145/3551624.3555302 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Motivated by COVID-19 vaccine allocation, where vulnerable subpopulations are
simultaneously more impacted in terms of health and more disadvantaged in terms
of access to the vaccine, we formalize and study the problem of resource
allocation when there are inherent access differences that correlate with
advantage and disadvantage. We identify reducing resource disparity as a key
goal in this context and show its role as a proxy to more nuanced downstream
impacts. We develop a concrete access model that helps quantify how a given
allocation translates to resource flow for the advantaged vs. the
disadvantaged, based on the access gap between them. We then provide a
methodology for access-aware allocation. Intuitively, the resulting allocation
leverages more vaccines in locations with higher vulnerable populations to
mitigate the access gap and reduce overall disparity. Surprisingly, knowledge
of the access gap is often not needed to perform access-aware allocation. To
support this formalism, we provide empirical evidence for our access model and
show that access-aware allocation can significantly reduce resource disparity
and thus improve downstream outcomes. We demonstrate this at various scales,
including at county, state, national, and global levels.
| [
{
"version": "v1",
"created": "Wed, 31 Jan 2024 05:25:12 GMT"
}
] | 1,706,832,000,000 | [
[
"Andrews",
"Kenya",
""
],
[
"Ohannessian",
"Mesrob",
""
],
[
"Berger-Wolf",
"Tanya",
""
]
] |
2402.00262 | Qun Ma | Qun Ma, Xiao Xue, Deyu Zhou, Xiangning Yu, Donghua Liu, Xuwen Zhang,
Zihan Zhao, Yifan Shen, Peilin Ji, Juanjuan Li, Gang Wang, Wanpeng Ma | Computational Experiments Meet Large Language Model Based Agents: A
Survey and Perspective | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational experiments have emerged as a valuable method for studying
complex systems, involving the algorithmization of counterfactuals. However,
accurately representing real social systems in Agent-based Modeling (ABM) is
challenging due to the diverse and intricate characteristics of humans,
including bounded rationality and heterogeneity. To address this limitation,
the integration of Large Language Models (LLMs) has been proposed, enabling
agents to possess anthropomorphic abilities such as complex reasoning and
autonomous learning. These agents, known as LLM-based Agent, offer the
potential to enhance the anthropomorphism lacking in ABM. Nonetheless, the
absence of explicit explainability in LLMs significantly hinders their
application in the social sciences. Conversely, computational experiments excel
in providing causal analysis of individual behaviors and complex phenomena.
Thus, combining computational experiments with LLM-based Agent holds
substantial research potential. This paper aims to present a comprehensive
exploration of this fusion. Primarily, it outlines the historical development
of agent structures and their evolution into artificial societies, emphasizing
their importance in computational experiments. Then it elucidates the
advantages that computational experiments and LLM-based Agents offer each
other, considering the perspectives of LLM-based Agent for computational
experiments and vice versa. Finally, this paper addresses the challenges and
future trends in this research domain, offering guidance for subsequent related
studies.
| [
{
"version": "v1",
"created": "Thu, 1 Feb 2024 01:17:46 GMT"
}
] | 1,706,832,000,000 | [
[
"Ma",
"Qun",
""
],
[
"Xue",
"Xiao",
""
],
[
"Zhou",
"Deyu",
""
],
[
"Yu",
"Xiangning",
""
],
[
"Liu",
"Donghua",
""
],
[
"Zhang",
"Xuwen",
""
],
[
"Zhao",
"Zihan",
""
],
[
"Shen",
"Yifan",
""
],
[
"Ji",
"Peilin",
""
],
[
"Li",
"Juanjuan",
""
],
[
"Wang",
"Gang",
""
],
[
"Ma",
"Wanpeng",
""
]
] |
2402.00468 | Biswajit Sadhu | Biswajit Sadhu, Trijit Sadhu, S. Anand | RadDQN: a Deep Q Learning-based Architecture for Finding Time-efficient
Minimum Radiation Exposure Pathway | 12 pages, 7 main figures, code link (GitHub) | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recent advancements in deep reinforcement learning (DRL) techniques have
sparked its multifaceted applications in the automation sector. Managing
complex decision-making problems with DRL encourages its use in the nuclear
industry for tasks such as optimizing radiation exposure to the personnel
during normal operating conditions and potential accidental scenarios. However,
the lack of efficient reward function and effective exploration strategy
thwarted its implementation in the development of radiation-aware autonomous
unmanned aerial vehicle (UAV) for achieving maximum radiation protection. Here,
in this article, we address these intriguing issues and introduce a deep
Q-learning based architecture (RadDQN) that operates on a radiation-aware
reward function to provide time-efficient minimum radiation-exposure pathway in
a radiation zone. We propose a set of unique exploration strategies that
fine-tune the extent of exploration and exploitation based on the state-wise
variation in radiation exposure during training. Further, we benchmark the
predicted path with grid-based deterministic method. We demonstrate that the
formulated reward function in conjugation with adequate exploration strategy is
effective in handling several scenarios with drastically different radiation
field distributions. When compared to vanilla DQN, our model achieves a
superior convergence rate and higher training stability.
| [
{
"version": "v1",
"created": "Thu, 1 Feb 2024 10:15:39 GMT"
}
] | 1,706,832,000,000 | [
[
"Sadhu",
"Biswajit",
""
],
[
"Sadhu",
"Trijit",
""
],
[
"Anand",
"S.",
""
]
] |
2402.00591 | Nicolas Lazzari | Nicolas Lazzari, Stefano De Giorgis, Aldo Gangemi, Valentina Presutti | Sandra -- A Neuro-Symbolic Reasoner Based On Descriptions And Situations | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presents sandra, a neuro-symbolic reasoner combining vectorial
representations with deductive reasoning. Sandra builds a vector space
constrained by an ontology and performs reasoning over it. The geometric nature
of the reasoner allows its combination with neural networks, bridging the gap
with symbolic knowledge representations. Sandra is based on the Description and
Situation (DnS) ontology design pattern, a formalization of frame semantics.
Given a set of facts (a situation) it allows to infer all possible perspectives
(descriptions) that can provide a plausible interpretation for it, even in
presence of incomplete information. We prove that our method is correct with
respect to the DnS model. We experiment with two different tasks and their
standard benchmarks, demonstrating that, without increasing complexity, sandra
(i) outperforms all the baselines (ii) provides interpretability in the
classification process, and (iii) allows control over the vector space, which
is designed a priori.
| [
{
"version": "v1",
"created": "Thu, 1 Feb 2024 13:37:53 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Feb 2024 08:58:41 GMT"
},
{
"version": "v3",
"created": "Mon, 25 Mar 2024 10:52:20 GMT"
}
] | 1,711,411,200,000 | [
[
"Lazzari",
"Nicolas",
""
],
[
"De Giorgis",
"Stefano",
""
],
[
"Gangemi",
"Aldo",
""
],
[
"Presutti",
"Valentina",
""
]
] |
2402.00738 | Guangzheng Hu | Guangzheng Hu, Yuanheng Zhu, Haoran Li, Dongbin Zhao | FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum
Markov Game | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many real-world applications involve some agents that fall into two teams,
with payoffs that are equal within the same team but of opposite sign across
the opponent team. The so-called two-team zero-sum Markov games (2t0sMGs) can
be resolved with reinforcement learning in recent years. However, existing
methods are thus inefficient in light of insufficient consideration of
intra-team credit assignment, data utilization and computational
intractability. In this paper, we propose the individual-global-minimax (IGMM)
principle to ensure the coherence between two-team minimax behaviors and the
individual greedy behaviors through Q functions in 2t0sMGs. Based on it, we
present a novel multi-agent reinforcement learning framework, Factorized
Multi-Agent MiniMax Q-Learning (FM3Q), which can factorize the joint minimax Q
function into individual ones and iteratively solve for the IGMM-satisfied
minimax Q functions for 2t0sMGs. Moreover, an online learning algorithm with
neural networks is proposed to implement FM3Q and obtain the deterministic and
decentralized minimax policies for two-team players. A theoretical analysis is
provided to prove the convergence of FM3Q. Empirically, we use three
environments to evaluate the learning efficiency and final performance of FM3Q
and show its superiority on 2t0sMGs.
| [
{
"version": "v1",
"created": "Thu, 1 Feb 2024 16:37:21 GMT"
}
] | 1,706,832,000,000 | [
[
"Hu",
"Guangzheng",
""
],
[
"Zhu",
"Yuanheng",
""
],
[
"Li",
"Haoran",
""
],
[
"Zhao",
"Dongbin",
""
]
] |
2402.00901 | Alex Grzankowski | Alex Grzankowski | Real Sparks of Artificial Intelligence and the Importance of Inner
Interpretability | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The present paper looks at one of the most thorough articles on the
intelligence of GPT, research conducted by engineers at Microsoft. Although
there is a great deal of value in their work, I will argue that, for familiar
philosophical reasons, their methodology, !Blackbox Interpretability"#is
wrongheaded. But there is a better way. There is an exciting and emerging
discipline of !Inner Interpretability"#(and specifically Mechanistic
Interpretability) that aims to uncover the internal activations and weights of
models in order to understand what they represent and the algorithms they
implement. In my view, a crucial mistake in Black-box Interpretability is the
failure to appreciate that how processes are carried out matters when it comes
to intelligence and understanding. I can#t pretend to have a full story that
provides both necessary and sufficient conditions for being intelligent, but I
do think that Inner Interpretability dovetails nicely with plausible
philosophical views of what intelligence requires. So the conclusion is modest,
but the important point in my view is seeing how to get the research on the
right track. Towards the end of the paper, I will show how some of the
philosophical concepts can be used to further refine how Inner Interpretability
is approached, so the paper helps draw out a profitable, future two-way
exchange between Philosophers and Computer Scientists.
| [
{
"version": "v1",
"created": "Wed, 31 Jan 2024 23:22:13 GMT"
}
] | 1,707,091,200,000 | [
[
"Grzankowski",
"Alex",
""
]
] |
2402.01276 | Jiaqi Shao | Jiaqi Shao, Tao Lin, Xuanyu Cao, Bing Luo | Federated Unlearning: a Perspective of Stability and Fairness | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores the multifaceted consequences of federated unlearning
(FU) with data heterogeneity. We introduce key metrics for FU assessment,
concentrating on verification, global stability, and local fairness, and
investigate the inherent trade-offs. Furthermore, we formulate the unlearning
process with data heterogeneity through an optimization framework. Our key
contribution lies in a comprehensive theoretical analysis of the trade-offs in
FU and provides insights into data heterogeneity's impacts on FU. Leveraging
these insights, we propose FU mechanisms to manage the trade-offs, guiding
further development for FU mechanisms. We empirically validate that our FU
mechanisms effectively balance trade-offs, confirming insights derived from our
theoretical analysis.
| [
{
"version": "v1",
"created": "Fri, 2 Feb 2024 10:05:25 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Feb 2024 16:11:29 GMT"
},
{
"version": "v3",
"created": "Mon, 12 Feb 2024 05:00:44 GMT"
},
{
"version": "v4",
"created": "Sat, 1 Jun 2024 15:18:50 GMT"
}
] | 1,717,459,200,000 | [
[
"Shao",
"Jiaqi",
""
],
[
"Lin",
"Tao",
""
],
[
"Cao",
"Xuanyu",
""
],
[
"Luo",
"Bing",
""
]
] |
2402.01499 | Willem van der Maden | Willem van der Maden, Derek Lomas, Paul Hekkert | Developing and Evaluating a Design Method for Positive Artificial
Intelligence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | As artificial intelligence (AI) continues advancing, ensuring positive
societal impacts becomes critical, especially as AI systems become increasingly
ubiquitous in various aspects of life. However, developing "AI for good" poses
substantial challenges around aligning systems with complex human values.
Presently, we lack mature methods for addressing these challenges. This article
presents and evaluates the Positive AI design method aimed at addressing this
gap. The method provides a human-centered process to translate wellbeing
aspirations into concrete practices. First, we explain the method's four key
steps: contextualizing, operationalizing, optimizing, and implementing
wellbeing supported by continuous measurement for feedback cycles. We then
present a multiple case study where novice designers applied the method,
revealing strengths and weaknesses related to efficacy and usability. Next, an
expert evaluation study assessed the quality of the resulting concepts, rating
them moderately high for feasibility, desirability, and plausibility of
achieving intended wellbeing benefits. Together, these studies provide
preliminary validation of the method's ability to improve AI design, while
surfacing areas needing refinement like developing support for complex steps.
Proposed adaptations such as examples and evaluation heuristics could address
weaknesses. Further research should examine sustained application over multiple
projects. This human-centered approach shows promise for realizing the vision
of 'AI for Wellbeing' that does not just avoid harm, but actively benefits
humanity.
| [
{
"version": "v1",
"created": "Fri, 2 Feb 2024 15:31:08 GMT"
},
{
"version": "v2",
"created": "Mon, 4 Mar 2024 12:52:13 GMT"
}
] | 1,709,596,800,000 | [
[
"van der Maden",
"Willem",
""
],
[
"Lomas",
"Derek",
""
],
[
"Hekkert",
"Paul",
""
]
] |
2402.01602 | Debarun Bhattacharjya | Debarun Bhattacharjya, Junkyu Lee, Don Joven Agravante, Balaji
Ganesan, Radu Marinescu | Foundation Model Sherpas: Guiding Foundation Models through Knowledge
and Reasoning | 9 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Foundation models (FMs) such as large language models have revolutionized the
field of AI by showing remarkable performance in various tasks. However, they
exhibit numerous limitations that prevent their broader adoption in many
real-world systems, which often require a higher bar for trustworthiness and
usability. Since FMs are trained using loss functions aimed at reconstructing
the training corpus in a self-supervised manner, there is no guarantee that the
model's output aligns with users' preferences for a specific task at hand. In
this survey paper, we propose a conceptual framework that encapsulates
different modes by which agents could interact with FMs and guide them suitably
for a set of tasks, particularly through knowledge augmentation and reasoning.
Our framework elucidates agent role categories such as updating the underlying
FM, assisting with prompting the FM, and evaluating the FM output. We also
categorize several state-of-the-art approaches into agent interaction
protocols, highlighting the nature and extent of involvement of the various
agent roles. The proposed framework provides guidance for future directions to
further realize the power of FMs in practical AI systems.
| [
{
"version": "v1",
"created": "Fri, 2 Feb 2024 18:00:35 GMT"
}
] | 1,707,091,200,000 | [
[
"Bhattacharjya",
"Debarun",
""
],
[
"Lee",
"Junkyu",
""
],
[
"Agravante",
"Don Joven",
""
],
[
"Ganesan",
"Balaji",
""
],
[
"Marinescu",
"Radu",
""
]
] |
2402.03640 | Abdelrahman Hosny | Abdelrahman Hosny, Sherief Reda | torchmSAT: A GPU-Accelerated Approximation To The Maximum Satisfiability
Problem | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The remarkable achievements of machine learning techniques in analyzing
discrete structures have drawn significant attention towards their integration
into combinatorial optimization algorithms. Typically, these methodologies
improve existing solvers by injecting learned models within the solving loop to
enhance the efficiency of the search process. In this work, we derive a single
differentiable function capable of approximating solutions for the Maximum
Satisfiability Problem (MaxSAT). Then, we present a novel neural network
architecture to model our differentiable function, and progressively solve
MaxSAT using backpropagation. This approach eliminates the need for labeled
data or a neural network training phase, as the training process functions as
the solving algorithm. Additionally, we leverage the computational power of
GPUs to accelerate these computations. Experimental results on challenging
MaxSAT instances show that our proposed methodology outperforms two existing
MaxSAT solvers, and is on par with another in terms of solution cost, without
necessitating any training or access to an underlying SAT solver. Given that
numerous NP-hard problems can be reduced to MaxSAT, our novel technique paves
the way for a new generation of solvers poised to benefit from neural network
GPU acceleration.
| [
{
"version": "v1",
"created": "Tue, 6 Feb 2024 02:33:00 GMT"
}
] | 1,707,264,000,000 | [
[
"Hosny",
"Abdelrahman",
""
],
[
"Reda",
"Sherief",
""
]
] |
2402.03824 | Giuseppe Paolo Dr | Giuseppe Paolo, Jonas Gonzalez-Billandon, Bal\'azs K\'egl | A call for embodied AI | Published in ICML 2024 Position paper track | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose Embodied AI as the next fundamental step in the pursuit of
Artificial General Intelligence, juxtaposing it against current AI
advancements, particularly Large Language Models. We traverse the evolution of
the embodiment concept across diverse fields - philosophy, psychology,
neuroscience, and robotics - to highlight how EAI distinguishes itself from the
classical paradigm of static learning. By broadening the scope of Embodied AI,
we introduce a theoretical framework based on cognitive architectures,
emphasizing perception, action, memory, and learning as essential components of
an embodied agent. This framework is aligned with Friston's active inference
principle, offering a comprehensive approach to EAI development. Despite the
progress made in the field of AI, substantial challenges, such as the
formulation of a novel AI learning theory and the innovation of advanced
hardware, persist. Our discussion lays down a foundational guideline for future
Embodied AI research. Highlighting the importance of creating Embodied AI
agents capable of seamless communication, collaboration, and coexistence with
humans and other intelligent entities within real-world environments, we aim to
steer the AI community towards addressing the multifaceted challenges and
seizing the opportunities that lie ahead in the quest for AGI.
| [
{
"version": "v1",
"created": "Tue, 6 Feb 2024 09:11:20 GMT"
},
{
"version": "v2",
"created": "Tue, 28 May 2024 15:07:37 GMT"
}
] | 1,716,940,800,000 | [
[
"Paolo",
"Giuseppe",
""
],
[
"Gonzalez-Billandon",
"Jonas",
""
],
[
"Kégl",
"Balázs",
""
]
] |
2402.04338 | Islambek Saymanov | Islambek Saymanov | Logical recognition method for solving the problem of identification in
the Internet of Things | I will rework and improve it and post it again | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new area of application of methods of algebra of logic and to valued logic,
which has emerged recently, is the problem of recognizing a variety of objects
and phenomena, medical or technical diagnostics, constructing modern machines,
checking test problems, etc., which can be reduced to constructing an optimal
extension of the logical function to the entire feature space. For example, in
logical recognition systems, logical methods based on discrete analysis and
propositional calculus based on it are used to build their own recognition
algorithms. In the general case, the use of a logical recognition method
provides for the presence of logical connections expressed by the optimal
continuation of a k-valued function over the entire feature space, in which the
variables are the logical features of the objects or phenomena being
recognized. The goal of this work is to develop a logical method for object
recognition consisting of a reference table with logical features and classes
of non-intersecting objects, which are specified as vectors from a given
feature space. The method consists of considering the reference table as a
logical function that is not defined everywhere and constructing an optimal
continuation of the logical function to the entire feature space, which
determines the extension of classes to the entire space.
| [
{
"version": "v1",
"created": "Tue, 6 Feb 2024 19:20:58 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Feb 2024 16:05:50 GMT"
}
] | 1,707,868,800,000 | [
[
"Saymanov",
"Islambek",
""
]
] |
2402.04370 | Yueyang Wang | Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Jussi P.P. Jokinen,
Antti Oulasvirta, Gustav Markkula | Pedestrian crossing decisions can be explained by bounded optimal
decision-making under noisy visual perception | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presents a model of pedestrian crossing decisions, based on the
theory of computational rationality. It is assumed that crossing decisions are
boundedly optimal, with bounds on optimality arising from human cognitive
limitations. While previous models of pedestrian behaviour have been either
'black-box' machine learning models or mechanistic models with explicit
assumptions about cognitive factors, we combine both approaches. Specifically,
we model mechanistically noisy human visual perception and assumed rewards in
crossing, but we use reinforcement learning to learn bounded optimal behaviour
policy. The model reproduces a larger number of known empirical phenomena than
previous models, in particular: (1) the effect of the time to arrival of an
approaching vehicle on whether the pedestrian accepts the gap, the effect of
the vehicle's speed on both (2) gap acceptance and (3) pedestrian timing of
crossing in front of yielding vehicles, and (4) the effect on this crossing
timing of the stopping distance of the yielding vehicle. Notably, our findings
suggest that behaviours previously framed as 'biases' in decision-making, such
as speed-dependent gap acceptance, might instead be a product of rational
adaptation to the constraints of visual perception. Our approach also permits
fitting the parameters of cognitive constraints and rewards per individual, to
better account for individual differences. To conclude, by leveraging both RL
and mechanistic modelling, our model offers novel insights about pedestrian
behaviour, and may provide a useful foundation for more accurate and scalable
pedestrian models.
| [
{
"version": "v1",
"created": "Tue, 6 Feb 2024 20:13:34 GMT"
}
] | 1,707,350,400,000 | [
[
"Wang",
"Yueyang",
""
],
[
"Srinivasan",
"Aravinda Ramakrishnan",
""
],
[
"Jokinen",
"Jussi P. P.",
""
],
[
"Oulasvirta",
"Antti",
""
],
[
"Markkula",
"Gustav",
""
]
] |
2402.04382 | Sopam Dasgupta | Sopam Dasgupta, Farhad Shakerin, Joaqu\'in Arias, Elmer Salazar, Gopal
Gupta | Counterfactual Generation with Answer Set Programming | 16 Pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Machine learning models that automate decision-making are increasingly being
used in consequential areas such as loan approvals, pretrial bail approval,
hiring, and many more. Unfortunately, most of these models are black-boxes,
i.e., they are unable to reveal how they reach these prediction decisions. A
need for transparency demands justification for such predictions. An affected
individual might also desire explanations to understand why a decision was
made. Ethical and legal considerations may further require informing the
individual of changes in the input attribute that could be made to produce a
desirable outcome. This paper focuses on the latter problem of automatically
generating counterfactual explanations. We propose a framework Counterfactual
Generation with s(CASP) (CFGS) that utilizes answer set programming (ASP) and
the s(CASP) goal-directed ASP system to automatically generate counterfactual
explanations from rules generated by rule-based machine learning (RBML)
algorithms. In our framework, we show how counterfactual explanations are
computed and justified by imagining worlds where some or all factual
assumptions are altered/changed. More importantly, we show how we can navigate
between these worlds, namely, go from our original world/scenario where we
obtain an undesired outcome to the imagined world/scenario where we obtain a
desired/favourable outcome.
| [
{
"version": "v1",
"created": "Tue, 6 Feb 2024 20:39:49 GMT"
}
] | 1,707,350,400,000 | [
[
"Dasgupta",
"Sopam",
""
],
[
"Shakerin",
"Farhad",
""
],
[
"Arias",
"Joaquín",
""
],
[
"Salazar",
"Elmer",
""
],
[
"Gupta",
"Gopal",
""
]
] |
2402.04938 | Luis Costero | Jennifer Hern\'andez-B\'ecares, Luis Costero, Pedro Pablo
G\'omez-Mart\'in | An approach to automated videogame beta testing | null | Entertainment Computing, Elsevier. 18. pp 79 to 92. (2017) | 10.1016/j.entcom.2016.08.002 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Videogames developed in the 1970s and 1980s were modest programs created in a
couple of months by a single person, who played the roles of designer, artist
and programmer. Since then, videogames have evolved to become a multi-million
dollar industry. Today, AAA game development involves hundreds of people
working together over several years. Management and engineering requirements
have changed at the same pace. Although many of the processes have been adapted
over time, this is not quite true for quality assurance tasks, which are still
done mainly manually by human beta testers due to the specific peculiarities of
videogames. This paper presents an approach to automate this beta testing.
| [
{
"version": "v1",
"created": "Wed, 7 Feb 2024 15:16:21 GMT"
}
] | 1,707,350,400,000 | [
[
"Hernández-Bécares",
"Jennifer",
""
],
[
"Costero",
"Luis",
""
],
[
"Gómez-Martín",
"Pedro Pablo",
""
]
] |
2402.05048 | Leonardo Bezerra | Leonardo C. T. Bezerra, Alexander E. I. Brownlee, Luana Ferraz
Alvarenga, Renan Cipriano Moioli, Thais Vasconcelos Batista | How VADER is your AI? Towards a definition of artificial intelligence
systems appropriate for regulation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Artificial intelligence (AI) has driven many information and communication
technology (ICT) breakthroughs. Nonetheless, the scope of ICT systems has
expanded far beyond AI since the Turing test proposal. Critically, recent AI
regulation proposals adopt AI definitions affecting ICT techniques, approaches,
and systems that are not AI. In some cases, even works from mathematics,
statistics, and engineering would be affected. Worryingly, AI misdefinitions
are observed from Western societies to the Global South. In this paper, we
propose a framework to score how validated as appropriately-defined for
regulation (VADER) an AI definition is. Our online, publicly-available VADER
framework scores the coverage of premises that should underlie AI definitions
for regulation, which aim to (i) reproduce principles observed in other
successful technology regulations, and (ii) include all AI techniques and
approaches while excluding non-AI works. Regarding the latter, our score is
based on a dataset of representative AI, non-AI ICT, and non-ICT examples. We
demonstrate our contribution by reviewing the AI regulation proposals of key
players, namely the United States, United Kingdom, European Union, and Brazil.
Importantly, none of the proposals assessed achieve the appropriateness score,
ranging from a revision need to a concrete risk to ICT systems and works from
other fields.
| [
{
"version": "v1",
"created": "Wed, 7 Feb 2024 17:41:15 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Feb 2024 12:02:45 GMT"
}
] | 1,707,955,200,000 | [
[
"Bezerra",
"Leonardo C. T.",
""
],
[
"Brownlee",
"Alexander E. I.",
""
],
[
"Alvarenga",
"Luana Ferraz",
""
],
[
"Moioli",
"Renan Cipriano",
""
],
[
"Batista",
"Thais Vasconcelos",
""
]
] |
2402.05829 | Jacek Karwowski | Raymond Douglas, Jacek Karwowski, Chan Bae, Andis Draguns, Victoria
Krakovna | Limitations of Agents Simulated by Predictive Models | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | There is increasing focus on adapting predictive models into agent-like
systems, most notably AI assistants based on language models. We outline two
structural reasons for why these models can fail when turned into agents.
First, we discuss auto-suggestive delusions. Prior work has shown theoretically
that models fail to imitate agents that generated the training data if the
agents relied on hidden observations: the hidden observations act as
confounding variables, and the models treat actions they generate as evidence
for nonexistent observations. Second, we introduce and formally study a
related, novel limitation: predictor-policy incoherence. When a model generates
a sequence of actions, the model's implicit prediction of the policy that
generated those actions can serve as a confounding variable. The result is that
models choose actions as if they expect future actions to be suboptimal,
causing them to be overly conservative. We show that both of those failures are
fixed by including a feedback loop from the environment, that is, re-training
the models on their own actions. We give simple demonstrations of both
limitations using Decision Transformers and confirm that empirical results
agree with our conceptual and formal analysis. Our treatment provides a
unifying view of those failure modes, and informs the question of why
fine-tuning offline learned policies with online learning makes them more
effective.
| [
{
"version": "v1",
"created": "Thu, 8 Feb 2024 17:08:08 GMT"
}
] | 1,707,436,800,000 | [
[
"Douglas",
"Raymond",
""
],
[
"Karwowski",
"Jacek",
""
],
[
"Bae",
"Chan",
""
],
[
"Draguns",
"Andis",
""
],
[
"Krakovna",
"Victoria",
""
]
] |
2402.06500 | Charles Assaad | Lei Zan, Charles K. Assaad, Emilie Devijver, Eric Gaussier | On the Fly Detection of Root Causes from Observed Data with Application
to IT Systems | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper introduces a new structural causal model tailored for representing
threshold-based IT systems and presents a new algorithm designed to rapidly
detect root causes of anomalies in such systems. When root causes are not
causally related, the method is proven to be correct; while an extension is
proposed based on the intervention of an agent to relax this assumption. Our
algorithm and its agent-based extension leverage causal discovery from offline
data and engage in subgraph traversal when encountering new anomalies in online
data. Our extensive experiments demonstrate the superior performance of our
methods, even when applied to data generated from alternative structural causal
models or real IT monitoring data.
| [
{
"version": "v1",
"created": "Fri, 9 Feb 2024 16:10:19 GMT"
}
] | 1,707,696,000,000 | [
[
"Zan",
"Lei",
""
],
[
"Assaad",
"Charles K.",
""
],
[
"Devijver",
"Emilie",
""
],
[
"Gaussier",
"Eric",
""
]
] |
2402.06673 | Yongchen Zhou | Yongchen Zhou, Richard Jiang | Advancing Explainable AI Toward Human-Like Intelligence: Forging the
Path to Artificial Brain | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The intersection of Artificial Intelligence (AI) and neuroscience in
Explainable AI (XAI) is pivotal for enhancing transparency and interpretability
in complex decision-making processes. This paper explores the evolution of XAI
methodologies, ranging from feature-based to human-centric approaches, and
delves into their applications in diverse domains, including healthcare and
finance. The challenges in achieving explainability in generative models,
ensuring responsible AI practices, and addressing ethical implications are
discussed. The paper further investigates the potential convergence of XAI with
cognitive sciences, the development of emotionally intelligent AI, and the
quest for Human-Like Intelligence (HLI) in AI systems. As AI progresses towards
Artificial General Intelligence (AGI), considerations of consciousness, ethics,
and societal impact become paramount. The ongoing pursuit of deciphering the
mysteries of the brain with AI and the quest for HLI represent transformative
endeavors, bridging technical advancements with multidisciplinary explorations
of human cognition.
| [
{
"version": "v1",
"created": "Wed, 7 Feb 2024 14:09:11 GMT"
}
] | 1,707,782,400,000 | [
[
"Zhou",
"Yongchen",
""
],
[
"Jiang",
"Richard",
""
]
] |
2402.06764 | Stefan Dernbach | Stefan Dernbach, Khushbu Agarwal, Alejandro Zuniga, Michael Henry,
Sutanay Choudhury | GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph
Alignment via Neighborhood Partitioning and Generative Subgraph Encoding | Published in AAAI Spring Symposium: AAAI-MAKE 2024 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Integrating large language models (LLMs) with knowledge graphs derived from
domain-specific data represents an important advancement towards more powerful
and factual reasoning. As these models grow more capable, it is crucial to
enable them to perform multi-step inferences over real-world knowledge graphs
while minimizing hallucination. While large language models excel at
conversation and text generation, their ability to reason over
domain-specialized graphs of interconnected entities remains limited. For
example, can we query a LLM to identify the optimal contact in a professional
network for a specific goal, based on relationships and attributes in a private
database? The answer is no--such capabilities lie beyond current methods.
However, this question underscores a critical technical gap that must be
addressed. Many high-value applications in areas such as science, security, and
e-commerce rely on proprietary knowledge graphs encoding unique structures,
relationships, and logical constraints. We introduce a fine-tuning framework
for developing Graph-aligned LAnguage Models (GLaM) that transforms a knowledge
graph into an alternate text representation with labeled question-answer pairs.
We demonstrate that grounding the models in specific graph-based knowledge
expands the models' capacity for structure-based reasoning. Our methodology
leverages the large-language model's generative capabilities to create the
dataset and proposes an efficient alternate to retrieval-augmented generation
styled methods.
| [
{
"version": "v1",
"created": "Fri, 9 Feb 2024 19:53:29 GMT"
},
{
"version": "v2",
"created": "Fri, 16 Feb 2024 17:23:56 GMT"
},
{
"version": "v3",
"created": "Wed, 17 Apr 2024 19:55:37 GMT"
}
] | 1,713,484,800,000 | [
[
"Dernbach",
"Stefan",
""
],
[
"Agarwal",
"Khushbu",
""
],
[
"Zuniga",
"Alejandro",
""
],
[
"Henry",
"Michael",
""
],
[
"Choudhury",
"Sutanay",
""
]
] |
2402.06811 | Andrew Smart | Andrew Smart, Ding Wang, Ellis Monk, Mark D\'iaz, Atoosa Kasirzadeh,
Erin Van Liemt, Sonja Schmer-Galunder | Discipline and Label: A WEIRD Genealogy and Social Theory of Data
Annotation | 18 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Data annotation remains the sine qua non of machine learning and AI. Recent
empirical work on data annotation has begun to highlight the importance of
rater diversity for fairness, model performance, and new lines of research have
begun to examine the working conditions for data annotation workers, the
impacts and role of annotator subjectivity on labels, and the potential
psychological harms from aspects of annotation work. This paper outlines a
critical genealogy of data annotation; starting with its psychological and
perceptual aspects. We draw on similarities with critiques of the rise of
computerized lab-based psychological experiments in the 1970's which question
whether these experiments permit the generalization of results beyond the
laboratory settings within which these results are typically obtained. Do data
annotations permit the generalization of results beyond the settings, or
locations, in which they were obtained? Psychology is overly reliant on
participants from Western, Educated, Industrialized, Rich, and Democratic
societies (WEIRD). Many of the people who work as data annotation platform
workers, however, are not from WEIRD countries; most data annotation workers
are based in Global South countries. Social categorizations and classifications
from WEIRD countries are imposed on non-WEIRD annotators through instructions
and tasks, and through them, on data, which is then used to train or evaluate
AI models in WEIRD countries. We synthesize evidence from several recent lines
of research and argue that data annotation is a form of automated social
categorization that risks entrenching outdated and static social categories
that are in reality dynamic and changing. We propose a framework for
understanding the interplay of the global social conditions of data annotation
with the subjective phenomenological experience of data annotation work.
| [
{
"version": "v1",
"created": "Fri, 9 Feb 2024 22:21:55 GMT"
}
] | 1,707,782,400,000 | [
[
"Smart",
"Andrew",
""
],
[
"Wang",
"Ding",
""
],
[
"Monk",
"Ellis",
""
],
[
"Díaz",
"Mark",
""
],
[
"Kasirzadeh",
"Atoosa",
""
],
[
"Van Liemt",
"Erin",
""
],
[
"Schmer-Galunder",
"Sonja",
""
]
] |
2402.06861 | Yansong Ning | Yansong Ning, Hao Liu | UrbanKGent: A Unified Large Language Model Agent Framework for Urban
Knowledge Graph Construction | Under review | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Urban knowledge graph has recently worked as an emerging building block to
distill critical knowledge from multi-sourced urban data for diverse urban
application scenarios. Despite its promising benefits, urban knowledge graph
construction (UrbanKGC) still heavily relies on manual effort, hindering its
potential advancement. This paper presents UrbanKGent, a unified large language
model agent framework, for urban knowledge graph construction. Specifically, we
first construct the knowledgeable instruction set for UrbanKGC tasks (such as
relational triplet extraction and knowledge graph completion) via
heterogeneity-aware and geospatial-infused instruction generation. Moreover, we
propose a tool-augmented iterative trajectory refinement module to enhance and
refine the trajectories distilled from GPT-4. Through hybrid instruction
fine-tuning with augmented trajectories on Llama-2-13B, we obtain the UrbanKGC
agent, UrbanKGent-13B. We perform a comprehensive evaluation on two real-world
datasets using both human and GPT-4 self-evaluation. The experimental results
demonstrate that UrbanKGent-13B not only can significantly outperform 21
baselines in UrbanKGC tasks, but also surpass the state-of-the-art LLM, GPT-4,
by more than 10\% with approximately 20 times lower cost. We deploy
UrbanKGent-13B to provide online services, which can construct an UrbanKG with
thousands of times richer relationships using only one-fifth of the data
compared with the existing benchmark. Our data, code, and opensource UrbanKGC
agent are available at https://github.com/usail-hkust/UrbanKGent.
| [
{
"version": "v1",
"created": "Sat, 10 Feb 2024 01:50:19 GMT"
}
] | 1,707,782,400,000 | [
[
"Ning",
"Yansong",
""
],
[
"Liu",
"Hao",
""
]
] |
2402.06929 | Jae Young Suh | Jae Young Suh, Minsoo Kwak, Soo Yong Kim, Hyoungseo Cho | Making a prototype of Seoul historical sites chatbot using Langchain | 4 pages, 4 figures, draft | null | 10.33140/JEEE.03.01.14 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we are going to share a draft of the development of a
conversational agent created to disseminate information about historical sites
located in the Seoul. The primary objective of the agent is to increase
awareness among visitors who are not familiar with Seoul, about the presence
and precise locations of valuable cultural heritage sites. It aims to promote a
basic understanding of Korea's rich and diverse cultural history. The agent is
thoughtfully designed for accessibility in English and utilizes data generously
provided by the Seoul Metropolitan Government. Despite the limited data volume,
it consistently delivers reliable and accurate responses, seamlessly aligning
with the available information. We have meticulously detailed the methodologies
employed in creating this agent and provided a comprehensive overview of its
underlying structure within the paper. Additionally, we delve into potential
improvements to enhance this initial version of the system, with a primary
emphasis on expanding the available data through our prompting. In conclusion,
we provide an in-depth discussion of our expectations regarding the future
impact of this agent in promoting and facilitating the sharing of historical
sites.
| [
{
"version": "v1",
"created": "Sat, 10 Feb 2024 11:38:09 GMT"
}
] | 1,709,683,200,000 | [
[
"Suh",
"Jae Young",
""
],
[
"Kwak",
"Minsoo",
""
],
[
"Kim",
"Soo Yong",
""
],
[
"Cho",
"Hyoungseo",
""
]
] |
2402.07016 | Yinghao Zhu | Yinghao Zhu, Changyu Ren, Shiyun Xie, Shukai Liu, Hangyuan Ji, Zixiang
Wang, Tao Sun, Long He, Zhoujun Li, Xi Zhu, Chengwei Pan | REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records
Analysis via Large Language Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The integration of multimodal Electronic Health Records (EHR) data has
significantly improved clinical predictive capabilities. Leveraging clinical
notes and multivariate time-series EHR, existing models often lack the medical
context relevent to clinical tasks, prompting the incorporation of external
knowledge, particularly from the knowledge graph (KG). Previous approaches with
KG knowledge have primarily focused on structured knowledge extraction,
neglecting unstructured data modalities and semantic high dimensional medical
knowledge. In response, we propose REALM, a Retrieval-Augmented Generation
(RAG) driven framework to enhance multimodal EHR representations that address
these limitations. Firstly, we apply Large Language Model (LLM) to encode long
context clinical notes and GRU model to encode time-series EHR data. Secondly,
we prompt LLM to extract task-relevant medical entities and match entities in
professionally labeled external knowledge graph (PrimeKG) with corresponding
medical knowledge. By matching and aligning with clinical standards, our
framework eliminates hallucinations and ensures consistency. Lastly, we propose
an adaptive multimodal fusion network to integrate extracted knowledge with
multimodal EHR data. Our extensive experiments on MIMIC-III mortality and
readmission tasks showcase the superior performance of our REALM framework over
baselines, emphasizing the effectiveness of each module. REALM framework
contributes to refining the use of multimodal EHR data in healthcare and
bridging the gap with nuanced medical context essential for informed clinical
predictions.
| [
{
"version": "v1",
"created": "Sat, 10 Feb 2024 18:27:28 GMT"
}
] | 1,707,782,400,000 | [
[
"Zhu",
"Yinghao",
""
],
[
"Ren",
"Changyu",
""
],
[
"Xie",
"Shiyun",
""
],
[
"Liu",
"Shukai",
""
],
[
"Ji",
"Hangyuan",
""
],
[
"Wang",
"Zixiang",
""
],
[
"Sun",
"Tao",
""
],
[
"He",
"Long",
""
],
[
"Li",
"Zhoujun",
""
],
[
"Zhu",
"Xi",
""
],
[
"Pan",
"Chengwei",
""
]
] |
2402.07049 | Behzad Akbari | Behzad Akbari, Mingfeng Yuan, Hao Wang, Haibin Zhu, Jinjun Shan | A Factor Graph Model of Trust for a Collaborative Multi-Agent System | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the field of Multi-Agent Systems (MAS), known for their openness,
dynamism, and cooperative nature, the ability to trust the resources and
services of other agents is crucial. Trust, in this setting, is the reliance
and confidence an agent has in the information, behaviors, intentions,
truthfulness, and capabilities of others within the system. Our paper
introduces a new graphical approach that utilizes factor graphs to represent
the interdependent behaviors and trustworthiness among agents. This includes
modeling the behavior of robots as a trajectory of actions using a Gaussian
process factor graph, which accounts for smoothness, obstacle avoidance, and
trust-related factors. Our method for evaluating trust is decentralized and
considers key interdependent sub-factors such as proximity safety, consistency,
and cooperation. The overall system comprises a network of factor graphs that
interact through trust-related factors and employs a Bayesian inference method
to dynamically assess trust-based decisions with informed consent. The
effectiveness of this method is validated via simulations and empirical tests
with autonomous robots navigating unsignalized intersections.
| [
{
"version": "v1",
"created": "Sat, 10 Feb 2024 21:44:28 GMT"
}
] | 1,707,782,400,000 | [
[
"Akbari",
"Behzad",
""
],
[
"Yuan",
"Mingfeng",
""
],
[
"Wang",
"Hao",
""
],
[
"Zhu",
"Haibin",
""
],
[
"Shan",
"Jinjun",
""
]
] |
2402.07140 | Yuyao Ge | Yuyao Ge, Shenghua Liu, Wenjie Feng, Lingrui Mei, Lizhe Chen, Xueqi
Cheng | Graph Descriptive Order Improves Reasoning with Large Language Model | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, large language models have achieved state-of-the-art
performance across multiple domains. However, the progress in the field of
graph reasoning with LLM remains limited. Our work delves into this gap by
thoroughly investigating graph reasoning with LLMs. In this work, we reveal the
impact of the order of graph description on LLMs' graph reasoning performance,
which significantly affects LLMs' reasoning abilities. By altering this order,
we enhance the performance of LLMs from 42.22\% to 70\%. Furthermore, we
introduce the Scaled Graph Reasoning benchmark for assessing LLMs' performance
across various graph sizes and evaluate the relationship between LLMs' graph
reasoning abilities and graph size. We discover that the graph reasoning
performance of LLMs does not monotonically decrease with the increase in graph
size. The experiments span several mainstream models, including GPT-3.5,
LLaMA-2-7B, and LLaMA-2-13B, to offer a comprehensive evaluation.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 09:46:24 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Feb 2024 03:13:55 GMT"
},
{
"version": "v3",
"created": "Sat, 24 Feb 2024 07:05:37 GMT"
}
] | 1,708,992,000,000 | [
[
"Ge",
"Yuyao",
""
],
[
"Liu",
"Shenghua",
""
],
[
"Feng",
"Wenjie",
""
],
[
"Mei",
"Lingrui",
""
],
[
"Chen",
"Lizhe",
""
],
[
"Cheng",
"Xueqi",
""
]
] |
2402.07166 | Arifa Khan | Arifa Khan, P. Saravanan and S.K Venkatesan | Social Evolution of Published Text and The Emergence of Artificial
Intelligence Through Large Language Models and The Problem of Toxicity and
Bias | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | We provide a birds eye view of the rapid developments in AI and Deep Learning
that has led to the path-breaking emergence of AI in Large Language Models. The
aim of this study is to place all these developments in a pragmatic broader
historical social perspective without any exaggerations while at the same time
without any pessimism that created the AI winter in the 1970s to 1990s. We also
at the same time point out toxicity, bias, memorization, sycophancy, logical
inconsistencies, hallucinations that exist just as a warning to the overly
optimistic. We note here that just as this emergence of AI seems to occur at a
threshold point in the number of neural connections or weights, it has also
been observed that human brain and especially the cortex region is nothing
special or extraordinary but simply a case of scaled-up version of the primate
brain and that even the human intelligence seems like an emergent phenomena of
scale.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 11:23:28 GMT"
},
{
"version": "v2",
"created": "Fri, 17 May 2024 07:12:12 GMT"
}
] | 1,716,163,200,000 | [
[
"Khan",
"Arifa",
""
],
[
"Saravanan",
"P.",
""
],
[
"Venkatesan",
"S. K",
""
]
] |
2402.07167 | Zehao Dong | Zehao Dong, Yixin Chen, Hiram Gay, Yao Hao, Geoffrey D. Hugo, Pamela
Samson, Tianyu Zhao | Large-Language-Model Empowered Dose Volume Histogram Prediction for
Intensity Modulated Radiotherapy | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Treatment planning is currently a patient specific, time-consuming, and
resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction
plays a critical role in automating this process. The geometric relationship
between DVHs in radiotherapy plans and organs-at-risk (OAR) and planning target
volume (PTV) has been well established. This study explores the potential of
deep learning models for predicting DVHs using images and subsequent human
intervention facilitated by a large-language model (LLM) to enhance the
planning quality. We propose a pipeline to convert unstructured images to a
structured graph consisting of image-patch nodes and dose nodes. A novel Dose
Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the
structured graph. The proposed DoseGNN is enhanced with the LLM to encode
massive knowledge from prescriptions and interactive instructions from
clinicians. In this study, we introduced an online human-AI collaboration
(OHAC) system as a practical implementation of the concept proposed for the
automation of intensity-modulated radiotherapy (IMRT) planning. In comparison
to the widely-employed DL models used in radiotherapy, DoseGNN achieved mean
square errors that were 80$\%$, 76$\%$ and 41.0$\%$ of those predicted by Swin
U-Net Transformer, 3D U-Net CNN and vanilla MLP, respectively. Moreover, the
LLM-empowered DoseGNN model facilitates seamless adjustment to treatment plans
through interaction with clinicians using natural language.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 11:24:09 GMT"
}
] | 1,707,782,400,000 | [
[
"Dong",
"Zehao",
""
],
[
"Chen",
"Yixin",
""
],
[
"Gay",
"Hiram",
""
],
[
"Hao",
"Yao",
""
],
[
"Hugo",
"Geoffrey D.",
""
],
[
"Samson",
"Pamela",
""
],
[
"Zhao",
"Tianyu",
""
]
] |
2402.07183 | Ryota Iijima | Ryota Iijima, Sayaka Shiota, Hitoshi Kiya | A Random Ensemble of Encrypted Vision Transformers for Adversarially
Robust Defense | 9 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks (DNNs) are well known to be vulnerable to adversarial
examples (AEs). In previous studies, the use of models encrypted with a secret
key was demonstrated to be robust against white-box attacks, but not against
black-box ones. In this paper, we propose a novel method using the vision
transformer (ViT) that is a random ensemble of encrypted models for enhancing
robustness against both white-box and black-box attacks. In addition, a
benchmark attack method, called AutoAttack, is applied to models to test
adversarial robustness objectively. In experiments, the method was demonstrated
to be robust against not only white-box attacks but also black-box ones in an
image classification task on the CIFAR-10 and ImageNet datasets. The method was
also compared with the state-of-the-art in a standardized benchmark for
adversarial robustness, RobustBench, and it was verified to outperform
conventional defenses in terms of clean accuracy and robust accuracy.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 12:35:28 GMT"
}
] | 1,707,782,400,000 | [
[
"Iijima",
"Ryota",
""
],
[
"Shiota",
"Sayaka",
""
],
[
"Kiya",
"Hitoshi",
""
]
] |
2402.07197 | Mengmei Zhang | Mengmei Zhang, Mingwei Sun, Peng Wang, Shen Fan, Yanhu Mo, Xiaoxiao
Xu, Hong Liu, Cheng Yang, Chuan Shi | GraphTranslator: Aligning Graph Model to Large Language Model for
Open-ended Tasks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and
instruction-following capabilities, have catalyzed a revolutionary
transformation across diverse fields, especially for open-ended tasks. While
the idea is less explored in the graph domain, despite the availability of
numerous powerful graph models (GMs), they are restricted to tasks in a
pre-defined form. Although several methods applying LLMs to graphs have been
proposed, they fail to simultaneously handle the pre-defined and open-ended
tasks, with LLM as a node feature enhancer or as a standalone predictor. To
break this dilemma, we propose to bridge the pretrained GM and LLM by a
Translator, named GraphTranslator, aiming to leverage GM to handle the
pre-defined tasks effectively and utilize the extended interface of LLMs to
offer various open-ended tasks for GM. To train such Translator, we propose a
Producer capable of constructing the graph-text alignment data along node
information, neighbor information and model information. By translating node
representation into tokens, GraphTranslator empowers an LLM to make predictions
based on language instructions, providing a unified perspective for both
pre-defined and open-ended tasks. Extensive results demonstrate the
effectiveness of our proposed GraphTranslator on zero-shot node classification.
The graph question answering experiments reveal our GraphTranslator potential
across a broad spectrum of open-ended tasks through language instructions. Our
code is available at: https://github.com/alibaba/GraphTranslator.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 13:24:13 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Feb 2024 09:25:37 GMT"
},
{
"version": "v3",
"created": "Tue, 20 Feb 2024 08:34:15 GMT"
},
{
"version": "v4",
"created": "Wed, 28 Feb 2024 02:42:35 GMT"
}
] | 1,709,164,800,000 | [
[
"Zhang",
"Mengmei",
""
],
[
"Sun",
"Mingwei",
""
],
[
"Wang",
"Peng",
""
],
[
"Fan",
"Shen",
""
],
[
"Mo",
"Yanhu",
""
],
[
"Xu",
"Xiaoxiao",
""
],
[
"Liu",
"Hong",
""
],
[
"Yang",
"Cheng",
""
],
[
"Shi",
"Chuan",
""
]
] |
2402.07199 | Bingqing Liu | Bingqing Liu, Xikun Huang | Link-aware link prediction over temporal graph by pattern recognition | 12 pages, one column | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A temporal graph can be considered as a stream of links, each of which
represents an interaction between two nodes at a certain time. On temporal
graphs, link prediction is a common task, which aims to answer whether the
query link is true or not. To do this task, previous methods usually focus on
the learning of representations of the two nodes in the query link. We point
out that the learned representation by their models may encode too much
information with side effects for link prediction because they have not
utilized the information of the query link, i.e., they are link-unaware. Based
on this observation, we propose a link-aware model: historical links and the
query link are input together into the following model layers to distinguish
whether this input implies a reasonable pattern that ends with the query link.
During this process, we focus on the modeling of link evolution patterns rather
than node representations. Experiments on six datasets show that our model
achieves strong performances compared with state-of-the-art baselines, and the
results of link prediction are interpretable. The code and datasets are
available on the project website: https://github.com/lbq8942/TGACN.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 13:26:06 GMT"
}
] | 1,707,782,400,000 | [
[
"Liu",
"Bingqing",
""
],
[
"Huang",
"Xikun",
""
]
] |
2402.07221 | Francis Rhys Ward | Francis Rhys Ward and Matt MacDermott and Francesco Belardinelli and
Francesca Toni and Tom Everitt | The Reasons that Agents Act: Intention and Instrumental Goals | AAMAS24 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Intention is an important and challenging concept in AI. It is important
because it underlies many other concepts we care about, such as agency,
manipulation, legal responsibility, and blame. However, ascribing intent to AI
systems is contentious, and there is no universally accepted theory of
intention applicable to AI agents. We operationalise the intention with which
an agent acts, relating to the reasons it chooses its decision. We introduce a
formal definition of intention in structural causal influence models, grounded
in the philosophy literature on intent and applicable to real-world machine
learning systems. Through a number of examples and results, we show that our
definition captures the intuitive notion of intent and satisfies desiderata
set-out by past work. In addition, we show how our definition relates to past
concepts, including actual causality, and the notion of instrumental goals,
which is a core idea in the literature on safe AI agents. Finally, we
demonstrate how our definition can be used to infer the intentions of
reinforcement learning agents and language models from their behaviour.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 14:39:40 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Feb 2024 11:45:37 GMT"
}
] | 1,708,041,600,000 | [
[
"Ward",
"Francis Rhys",
""
],
[
"MacDermott",
"Matt",
""
],
[
"Belardinelli",
"Francesco",
""
],
[
"Toni",
"Francesca",
""
],
[
"Everitt",
"Tom",
""
]
] |
2402.07226 | Sungyoon Kim | Sungyoon Kim, Yunseon Choi, Daiki E. Matsunaga, and Kee-Eung Kim | Stitching Sub-Trajectories with Conditional Diffusion Model for
Goal-Conditioned Offline RL | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Offline Goal-Conditioned Reinforcement Learning (Offline GCRL) is an
important problem in RL that focuses on acquiring diverse goal-oriented skills
solely from pre-collected behavior datasets. In this setting, the reward
feedback is typically absent except when the goal is achieved, which makes it
difficult to learn policies especially from a finite dataset of suboptimal
behaviors. In addition, realistic scenarios involve long-horizon planning,
which necessitates the extraction of useful skills within sub-trajectories.
Recently, the conditional diffusion model has been shown to be a promising
approach to generate high-quality long-horizon plans for RL. However, their
practicality for the goal-conditioned setting is still limited due to a number
of technical assumptions made by the methods. In this paper, we propose SSD
(Sub-trajectory Stitching with Diffusion), a model-based offline GCRL method
that leverages the conditional diffusion model to address these limitations. In
summary, we use the diffusion model that generates future plans conditioned on
the target goal and value, with the target value estimated from the
goal-relabeled offline dataset. We report state-of-the-art performance in the
standard benchmark set of GCRL tasks, and demonstrate the capability to
successfully stitch the segments of suboptimal trajectories in the offline data
to generate high-quality plans.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 15:23:13 GMT"
}
] | 1,707,782,400,000 | [
[
"Kim",
"Sungyoon",
""
],
[
"Choi",
"Yunseon",
""
],
[
"Matsunaga",
"Daiki E.",
""
],
[
"Kim",
"Kee-Eung",
""
]
] |
2402.07234 | Xin Tong | Xin Tong, Bo Jin, Zhi Lin, Binjun Wang, Ting Yu and Qiang Cheng | CPSDBench: A Large Language Model Evaluation Benchmark and Baseline for
Chinese Public Security Domain | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have demonstrated significant potential and
effectiveness across multiple application domains. To assess the performance of
mainstream LLMs in public security tasks, this study aims to construct a
specialized evaluation benchmark tailored to the Chinese public security
domain--CPSDbench. CPSDbench integrates datasets related to public security
collected from real-world scenarios, supporting a comprehensive assessment of
LLMs across four key dimensions: text classification, information extraction,
question answering, and text generation. Furthermore, this study introduces a
set of innovative evaluation metrics designed to more precisely quantify the
efficacy of LLMs in executing tasks related to public security. Through the
in-depth analysis and evaluation conducted in this research, we not only
enhance our understanding of the performance strengths and limitations of
existing models in addressing public security issues but also provide
references for the future development of more accurate and customized LLM
models targeted at applications in this field.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 15:56:03 GMT"
},
{
"version": "v2",
"created": "Sun, 3 Mar 2024 01:26:01 GMT"
},
{
"version": "v3",
"created": "Thu, 21 Mar 2024 12:39:09 GMT"
}
] | 1,711,065,600,000 | [
[
"Tong",
"Xin",
""
],
[
"Jin",
"Bo",
""
],
[
"Lin",
"Zhi",
""
],
[
"Wang",
"Binjun",
""
],
[
"Yu",
"Ting",
""
],
[
"Cheng",
"Qiang",
""
]
] |
2402.07327 | Minoo Shayaninasab | Minoo Shayaninasab, Bagher Babaali | Multi-Modal Emotion Recognition by Text, Speech and Video Using
Pretrained Transformers | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Due to the complex nature of human emotions and the diversity of emotion
representation methods in humans, emotion recognition is a challenging field.
In this research, three input modalities, namely text, audio (speech), and
video, are employed to generate multimodal feature vectors. For generating
features for each of these modalities, pre-trained Transformer models with
fine-tuning are utilized. In each modality, a Transformer model is used with
transfer learning to extract feature and emotional structure. These features
are then fused together, and emotion recognition is performed using a
classifier. To select an appropriate fusion method and classifier, various
feature-level and decision-level fusion techniques have been experimented with,
and ultimately, the best model, which combines feature-level fusion by
concatenating feature vectors and classification using a Support Vector Machine
on the IEMOCAP multimodal dataset, achieves an accuracy of 75.42%. Keywords:
Multimodal Emotion Recognition, IEMOCAP, Self-Supervised Learning, Transfer
Learning, Transformer.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 23:27:24 GMT"
}
] | 1,707,782,400,000 | [
[
"Shayaninasab",
"Minoo",
""
],
[
"Babaali",
"Bagher",
""
]
] |
2402.07398 | Dongsheng Zhu | Dongsheng Zhu, Xunzhu Tang, Weidong Han, Jinghui Lu, Yukun Zhao,
Guoliang Xing, Junfeng Wang, Dawei Yin | VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language
Models with Autonomous Instruction Optimization | Accepted to NAACL2024 main conference | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presents VisLingInstruct, a novel approach to advancing
Multi-Modal Language Models (MMLMs) in zero-shot learning. Current MMLMs show
impressive zero-shot abilities in multi-modal tasks, but their performance
depends heavily on the quality of instructions. VisLingInstruct tackles this by
autonomously evaluating and optimizing instructional texts through In-Context
Learning, improving the synergy between visual perception and linguistic
expression in MMLMs. Alongside this instructional advancement, we have also
optimized the visual feature extraction modules in MMLMs, further augmenting
their responsiveness to textual cues. Our comprehensive experiments on MMLMs,
based on FlanT5 and Vicuna, show that VisLingInstruct significantly improves
zero-shot performance in visual multi-modal tasks. Notably, it achieves a 13.1%
and 9% increase in accuracy over the prior state-of-the-art on the TextVQA and
HatefulMemes datasets.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 04:13:16 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Mar 2024 14:30:14 GMT"
}
] | 1,710,460,800,000 | [
[
"Zhu",
"Dongsheng",
""
],
[
"Tang",
"Xunzhu",
""
],
[
"Han",
"Weidong",
""
],
[
"Lu",
"Jinghui",
""
],
[
"Zhao",
"Yukun",
""
],
[
"Xing",
"Guoliang",
""
],
[
"Wang",
"Junfeng",
""
],
[
"Yin",
"Dawei",
""
]
] |
2402.07418 | Sangwoo Shin | Sangwoo Shin, Minjong Yoo, Jeongwoo Lee, Honguk Woo | SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy
Adaptation | AAAI 2024 Camera-ready version | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This work explores the zero-shot adaptation capability of semantic skills,
semantically interpretable experts' behavior patterns, in cross-domain
settings, where a user input in interleaved multi-modal snippets can prompt a
new long-horizon task for different domains. In these cross-domain settings, we
present a semantic skill translator framework SemTra which utilizes a set of
multi-modal models to extract skills from the snippets, and leverages the
reasoning capabilities of a pretrained language model to adapt these extracted
skills to the target domain. The framework employs a two-level hierarchy for
adaptation: task adaptation and skill adaptation. During task adaptation,
seq-to-seq translation by the language model transforms the extracted skills
into a semantic skill sequence, which is tailored to fit the cross-domain
contexts. Skill adaptation focuses on optimizing each semantic skill for the
target domain context, through parametric instantiations that are facilitated
by language prompting and contrastive learning-based context inferences. This
hierarchical adaptation empowers the framework to not only infer a complex task
specification in one-shot from the interleaved multi-modal snippets, but also
adapt it to new domains with zero-shot learning abilities. We evaluate our
framework with Meta-World, Franka Kitchen, RLBench, and CARLA environments. The
results clarify the framework's superiority in performing long-horizon tasks
and adapting to different domains, showing its broad applicability in practical
use cases, such as cognitive robots interpreting abstract instructions and
autonomous vehicles operating under varied configurations.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 05:46:10 GMT"
}
] | 1,707,782,400,000 | [
[
"Shin",
"Sangwoo",
""
],
[
"Yoo",
"Minjong",
""
],
[
"Lee",
"Jeongwoo",
""
],
[
"Woo",
"Honguk",
""
]
] |
2402.07420 | Hideaki Takahashi | Hideaki Takahashi and Alex Fukunaga | On the Transit Obfuscation Problem | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Concealing an intermediate point on a route or visible from a route is an
important goal in some transportation and surveillance scenarios. This paper
studies the Transit Obfuscation Problem, the problem of traveling from some
start location to an end location while "covering" a specific transit point
that needs to be concealed from adversaries. We propose the notion of transit
anonymity, a quantitative guarantee of the anonymity of a specific transit
point, even with a powerful adversary with full knowledge of the path planning
algorithm. We propose and evaluate planning/search algorithms that satisfy this
anonymity criterion.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 05:48:52 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Feb 2024 07:02:05 GMT"
}
] | 1,707,868,800,000 | [
[
"Takahashi",
"Hideaki",
""
],
[
"Fukunaga",
"Alex",
""
]
] |
2402.07422 | Chufeng Jiang | Tianrui Liu, Changxin Xu, Yuxin Qiao, Chufeng Jiang, Weisheng Chen | News Recommendation with Attention Mechanism | 7 pages, Journal of Industrial Engineering and Applied Science | Journal of Industrial Engineering and Applied Science 2024 | 10.5281/zenodo.10635481 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores the area of news recommendation, a key component of
online information sharing. Initially, we provide a clear introduction to news
recommendation, defining the core problem and summarizing current methods and
notable recent algorithms. We then present our work on implementing the NRAM
(News Recommendation with Attention Mechanism), an attention-based approach for
news recommendation, and assess its effectiveness. Our evaluation shows that
NRAM has the potential to significantly improve how news content is
personalized for users on digital news platforms.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 05:56:12 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Feb 2024 02:46:17 GMT"
}
] | 1,708,473,600,000 | [
[
"Liu",
"Tianrui",
""
],
[
"Xu",
"Changxin",
""
],
[
"Qiao",
"Yuxin",
""
],
[
"Jiang",
"Chufeng",
""
],
[
"Chen",
"Weisheng",
""
]
] |
2402.07429 | Chufeng Jiang | Tianrui Liu, Changxin Xu, Yuxin Qiao, Chufeng Jiang, Jiqiang Yu | Particle Filter SLAM for Vehicle Localization | 6 pages, Journal of Industrial Engineering and Applied Science | Journal of Industrial Engineering and Applied Science 2024 | 10.5281/zenodo.10635489 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simultaneous Localization and Mapping (SLAM) presents a formidable challenge
in robotics, involving the dynamic construction of a map while concurrently
determining the precise location of the robotic agent within an unfamiliar
environment. This intricate task is further compounded by the inherent
"chicken-and-egg" dilemma, where accurate mapping relies on a dependable
estimation of the robot's location, and vice versa. Moreover, the computational
intensity of SLAM adds an additional layer of complexity, making it a crucial
yet demanding topic in the field. In our research, we address the challenges of
SLAM by adopting the Particle Filter SLAM method. Our approach leverages
encoded data and fiber optic gyro (FOG) information to enable precise
estimation of vehicle motion, while lidar technology contributes to
environmental perception by providing detailed insights into surrounding
obstacles. The integration of these data streams culminates in the
establishment of a Particle Filter SLAM framework, representing a key endeavor
in this paper to effectively navigate and overcome the complexities associated
with simultaneous localization and mapping in robotic systems.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 06:06:09 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Feb 2024 02:42:33 GMT"
}
] | 1,708,473,600,000 | [
[
"Liu",
"Tianrui",
""
],
[
"Xu",
"Changxin",
""
],
[
"Qiao",
"Yuxin",
""
],
[
"Jiang",
"Chufeng",
""
],
[
"Yu",
"Jiqiang",
""
]
] |
2402.07442 | Ray Ito | Ray Ito, Junichiro Takahashi | Game Agent Driven by Free-Form Text Command: Using LLM-based Code
Generation and Behavior Branch | This paper is posted at JSAI 2024 Conference | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Several attempts have been made to implement text command control for game
agents. However, current technologies are limited to processing predefined
format commands. This paper proposes a pioneering text command control system
for a game agent that can understand natural language commands expressed in
free-form. The proposed system uses a large language model (LLM) for code
generation to interpret and transform natural language commands into behavior
branch, a proposed knowledge expression based on behavior trees, which
facilitates execution by the game agent. This study conducted empirical
validation within a game environment that simulates a Pok\'emon game and
involved multiple participants. The results confirmed the system's ability to
understand and carry out natural language commands, representing a noteworthy
in the realm of real-time language interactive game agents.
Notice for the use of this material. The copyright of this material is
retained by the Japanese Society for Artificial Intelligence (JSAI). This
material is published here with the agreement of JSAI. Please be complied with
Copyright Law of Japan if any users wish to reproduce, make derivative work,
distribute or make available to the public any part or whole thereof. All
Rights Reserved, Copyright (C) The Japanese Society for Artificial
Intelligence.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 06:49:48 GMT"
}
] | 1,707,782,400,000 | [
[
"Ito",
"Ray",
""
],
[
"Takahashi",
"Junichiro",
""
]
] |
2402.07456 | Chengcheng Han | Zhiyong Wu, Chengcheng Han, Zichen Ding, Zhenmin Weng, Zhoumianze Liu,
Shunyu Yao, Tao Yu and Lingpeng Kong | OS-Copilot: Towards Generalist Computer Agents with Self-Improvement | Project page: https://os-copilot.github.io | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autonomous interaction with the computer has been a longstanding challenge
with great potential, and the recent proliferation of large language models
(LLMs) has markedly accelerated progress in building digital agents. However,
most of these agents are designed to interact with a narrow domain, such as a
specific software or website. This narrow focus constrains their applicability
for general computer tasks. To this end, we introduce OS-Copilot, a framework
to build generalist agents capable of interfacing with comprehensive elements
in an operating system (OS), including the web, code terminals, files,
multimedia, and various third-party applications. We use OS-Copilot to create
FRIDAY, a self-improving embodied agent for automating general computer tasks.
On GAIA, a general AI assistants benchmark, FRIDAY outperforms previous methods
by 35%, showcasing strong generalization to unseen applications via accumulated
skills from previous tasks. We also present numerical and quantitative evidence
that FRIDAY learns to control and self-improve on Excel and Powerpoint with
minimal supervision. Our OS-Copilot framework and empirical findings provide
infrastructure and insights for future research toward more capable and
general-purpose computer agents.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 07:29:22 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Feb 2024 09:30:48 GMT"
}
] | 1,708,041,600,000 | [
[
"Wu",
"Zhiyong",
""
],
[
"Han",
"Chengcheng",
""
],
[
"Ding",
"Zichen",
""
],
[
"Weng",
"Zhenmin",
""
],
[
"Liu",
"Zhoumianze",
""
],
[
"Yao",
"Shunyu",
""
],
[
"Yu",
"Tao",
""
],
[
"Kong",
"Lingpeng",
""
]
] |
2402.07477 | Ali Rostami | Ali Rostami, Ramesh Jain, Amir M. Rahmani | Food Recommendation as Language Processing (F-RLP): A Personalized and
Contextual Paradigm | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | State-of-the-art rule-based and classification-based food recommendation
systems face significant challenges in becoming practical and useful. This
difficulty arises primarily because most machine learning models struggle with
problems characterized by an almost infinite number of classes and a limited
number of samples within an unbalanced dataset. Conversely, the emergence of
Large Language Models (LLMs) as recommendation engines offers a promising
avenue. However, a general-purpose Recommendation as Language Processing (RLP)
approach lacks the critical components necessary for effective food
recommendations. To address this gap, we introduce Food Recommendation as
Language Processing (F-RLP), a novel framework that offers a food-specific,
tailored infrastructure. F-RLP leverages the capabilities of LLMs to maximize
their potential, thereby paving the way for more accurate, personalized food
recommendations.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 08:32:29 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Feb 2024 12:11:44 GMT"
}
] | 1,707,955,200,000 | [
[
"Rostami",
"Ali",
""
],
[
"Jain",
"Ramesh",
""
],
[
"Rahmani",
"Amir M.",
""
]
] |
2402.07507 | Sarah Almeida Carneiro | Sarah Almeida Carneiro (LIGM), Giovanni Chierchia (LIGM), Aurelie
Pirayre (IFPEN), Laurent Najman (LIGM) | Clustering Dynamics for Improved Speed Prediction Deriving from
Topographical GPS Registrations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A persistent challenge in the field of Intelligent Transportation Systems is
to extract accurate traffic insights from geographic regions with scarce or no
data coverage. To this end, we propose solutions for speed prediction using
sparse GPS data points and their associated topographical and road design
features. Our goal is to investigate whether we can use similarities in the
terrain and infrastructure to train a machine learning model that can predict
speed in regions where we lack transportation data. For this we create a
Temporally Orientated Speed Dictionary Centered on Topographically Clustered
Roads, which helps us to provide speed correlations to selected feature
configurations. Our results show qualitative and quantitative improvement over
new and standard regression methods. The presented framework provides a fresh
perspective on devising strategies for missing data traffic analysis.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 09:28:16 GMT"
}
] | 1,707,782,400,000 | [
[
"Carneiro",
"Sarah Almeida",
"",
"LIGM"
],
[
"Chierchia",
"Giovanni",
"",
"LIGM"
],
[
"Pirayre",
"Aurelie",
"",
"IFPEN"
],
[
"Najman",
"Laurent",
"",
"LIGM"
]
] |
2402.07772 | My H Dinh | My H Dinh and James Kotary and Ferdinando Fioretto | End-to-End Learning for Fair Multiobjective Optimization Under
Uncertainty | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many decision processes in artificial intelligence and operations research
are modeled by parametric optimization problems whose defining parameters are
unknown and must be inferred from observable data. The Predict-Then-Optimize
(PtO) paradigm in machine learning aims to maximize downstream decision quality
by training the parametric inference model end-to-end with the subsequent
constrained optimization. This requires backpropagation through the
optimization problem using approximation techniques specific to the problem's
form, especially for nondifferentiable linear and mixed-integer programs. This
paper extends the PtO methodology to optimization problems with
nondifferentiable Ordered Weighted Averaging (OWA) objectives, known for their
ability to ensure properties of fairness and robustness in decision models.
Through a collection of training techniques and proposed application settings,
it shows how optimization of OWA functions can be effectively integrated with
parametric prediction for fair and robust optimization under uncertainty.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 16:33:35 GMT"
}
] | 1,707,782,400,000 | [
[
"Dinh",
"My H",
""
],
[
"Kotary",
"James",
""
],
[
"Fioretto",
"Ferdinando",
""
]
] |
2402.07799 | Alberto Pozanco | Alberto Pozanco, Ramon Fraga Pereira, Daniel Borrajo | Generalising Planning Environment Redesign | Paper accepted at AAAI'24 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Environment Design, one interested party seeks to affect another agent's
decisions by applying changes to the environment. Most research on planning
environment (re)design assumes the interested party's objective is to
facilitate the recognition of goals and plans, and search over the space of
environment modifications to find the minimal set of changes that simplify
those tasks and optimise a particular metric. This search space is usually
intractable, so existing approaches devise metric-dependent pruning techniques
for performing search more efficiently. This results in approaches that are not
able to generalise across different objectives and/or metrics. In this paper,
we argue that the interested party could have objectives and metrics that are
not necessarily related to recognising agents' goals or plans. Thus, to
generalise the task of Planning Environment Redesign, we develop a general
environment redesign approach that is metric-agnostic and leverages recent
research on top-quality planning to efficiently redesign planning environments
according to any interested party's objective and metric. Experiments over a
set of environment redesign benchmarks show that our general approach
outperforms existing approaches when using well-known metrics, such as
facilitating the recognition of goals, as well as its effectiveness when
solving environment redesign tasks that optimise a novel set of different
metrics.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 17:03:58 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Feb 2024 14:01:55 GMT"
}
] | 1,707,955,200,000 | [
[
"Pozanco",
"Alberto",
""
],
[
"Pereira",
"Ramon Fraga",
""
],
[
"Borrajo",
"Daniel",
""
]
] |
2402.07822 | Sarah L. Thomson | Sarah L. Thomson, L\'eni K. Le Goff, Emma Hart, Edgar Buchanan | Understanding fitness landscapes in morpho-evolution via local optima
networks | Submitted to GECCO-2024 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Morpho-evolution (ME) refers to the simultaneous optimisation of a robot's
design and controller to maximise performance given a task and environment.
Many genetic encodings have been proposed which are capable of representing
design and control. Previous research has provided empirical comparisons
between encodings in terms of their performance with respect to an objective
function and the diversity of designs that are evaluated, however there has
been no attempt to explain the observed findings. We address this by applying
Local Optima Network (LON) analysis to investigate the structure of the fitness
landscapes induced by three different encodings when evolving a robot for a
locomotion task, shedding new light on the ease by which different fitness
landscapes can be traversed by a search process. This is the first time LON
analysis has been applied in the field of ME despite its popularity in
combinatorial optimisation domains; the findings will facilitate design of new
algorithms or operators that are customised to ME landscapes in the future.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 17:26:35 GMT"
}
] | 1,707,782,400,000 | [
[
"Thomson",
"Sarah L.",
""
],
[
"Goff",
"Léni K. Le",
""
],
[
"Hart",
"Emma",
""
],
[
"Buchanan",
"Edgar",
""
]
] |
2402.07877 | Yangxinyu Xie | Yangxinyu Xie, Tanwi Mallick, Joshua David Bergerson, John K.
Hutchison, Duane R. Verner, Jordan Branham, M. Ross Alexander, Robert B.
Ross, Yan Feng, Leslie-Anne Levy, Weijie Su | WildfireGPT: Tailored Large Language Model for Wildfire Analysis | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The recent advancement of large language models (LLMs) represents a
transformational capability at the frontier of artificial intelligence (AI) and
machine learning (ML). However, LLMs are generalized models, trained on
extensive text corpus, and often struggle to provide context-specific
information, particularly in areas requiring specialized knowledge such as
wildfire details within the broader context of climate change. For
decision-makers and policymakers focused on wildfire resilience and adaptation,
it is crucial to obtain responses that are not only precise but also
domain-specific, rather than generic. To that end, we developed WildfireGPT, a
prototype LLM agent designed to transform user queries into actionable insights
on wildfire risks. We enrich WildfireGPT by providing additional context such
as climate projections and scientific literature to ensure its information is
current, relevant, and scientifically accurate. This enables WildfireGPT to be
an effective tool for delivering detailed, user-specific insights on wildfire
risks to support a diverse set of end users, including researchers, engineers,
urban planners, emergency managers, and infrastructure operators.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 18:41:55 GMT"
}
] | 1,707,782,400,000 | [
[
"Xie",
"Yangxinyu",
""
],
[
"Mallick",
"Tanwi",
""
],
[
"Bergerson",
"Joshua David",
""
],
[
"Hutchison",
"John K.",
""
],
[
"Verner",
"Duane R.",
""
],
[
"Branham",
"Jordan",
""
],
[
"Alexander",
"M. Ross",
""
],
[
"Ross",
"Robert B.",
""
],
[
"Feng",
"Yan",
""
],
[
"Levy",
"Leslie-Anne",
""
],
[
"Su",
"Weijie",
""
]
] |
2402.08115 | Karthik Valmeekam | Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati | On the Self-Verification Limitations of Large Language Models on
Reasoning and Planning Tasks | arXiv admin note: text overlap with arXiv:2310.12397 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples--ranging from multiplication to simple planning--there persists
a wide spread belief that LLMs can self-critique and improve their own
solutions in an iterative fashion. This belief seemingly rests on the
assumption that verification of correctness should be easier than generation--a
rather classical argument from computational complexity--which should be
irrelevant to LLMs to the extent that what they are doing is approximate
retrieval. In this paper, we set out to systematically investigate the
effectiveness of iterative prompting in the context of reasoning and planning.
We present a principled empirical study of the performance of GPT-4 in three
domains: Game of 24, Graph Coloring, and STRIPS planning. We experiment both
with the model critiquing its own answers and with an external correct reasoner
verifying proposed solutions. In each case, we analyze whether the content of
criticisms actually affects bottom line performance, and whether we can ablate
elements of the augmented system without losing performance. We observe
significant performance collapse with self-critique, significant performance
gains with sound external verification, but that the content of critique
doesn't matter to the performance of the system. In fact, merely re-prompting
with a sound verifier maintains most of the benefits of more involved setups.
| [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 23:11:01 GMT"
}
] | 1,707,868,800,000 | [
[
"Stechly",
"Kaya",
""
],
[
"Valmeekam",
"Karthik",
""
],
[
"Kambhampati",
"Subbarao",
""
]
] |
2402.08145 | Rushang Karia | Rushang Karia, Pulkit Verma, Alberto Speranzon, Siddharth Srivastava | Epistemic Exploration for Generalizable Planning and Learning in
Non-Stationary Settings | To appear at ICAPS-24 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a new approach for continual planning and model
learning in non-stationary stochastic environments expressed using relational
representations. Such capabilities are essential for the deployment of
sequential decision-making systems in the uncertain, constantly evolving real
world. Working in such practical settings with unknown (and non-stationary)
transition systems and changing tasks, the proposed framework models gaps in
the agent's current state of knowledge and uses them to conduct focused,
investigative explorations. Data collected using these explorations is used for
learning generalizable probabilistic models for solving the current task
despite continual changes in the environment dynamics. Empirical evaluations on
several benchmark domains show that this approach significantly outperforms
planning and RL baselines in terms of sample complexity in non-stationary
settings. Theoretical results show that the system reverts to exhibit desirable
convergence properties when stationarity holds.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 00:50:06 GMT"
}
] | 1,707,868,800,000 | [
[
"Karia",
"Rushang",
""
],
[
"Verma",
"Pulkit",
""
],
[
"Speranzon",
"Alberto",
""
],
[
"Srivastava",
"Siddharth",
""
]
] |
2402.08178 | Jae-Woo Choi | Jae-Woo Choi and Youngwoo Yoon and Hyobin Ong and Jaehong Kim and
Minsu Jang | LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied
Agents | ICLR 2024. Code: https://github.com/lbaa2022/LLMTaskPlanning | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs) have recently received considerable attention as
alternative solutions for task planning. However, comparing the performance of
language-oriented task planners becomes difficult, and there exists a dearth of
detailed exploration regarding the effects of various factors such as
pre-trained model selection and prompt construction. To address this, we
propose a benchmark system for automatically quantifying performance of task
planning for home-service embodied agents. Task planners are tested on two
pairs of datasets and simulators: 1) ALFRED and AI2-THOR, 2) an extension of
Watch-And-Help and VirtualHome. Using the proposed benchmark system, we perform
extensive experiments with LLMs and prompts, and explore several enhancements
of the baseline planner. We expect that the proposed benchmark tool would
accelerate the development of language-oriented task planners.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 02:28:57 GMT"
}
] | 1,707,868,800,000 | [
[
"Choi",
"Jae-Woo",
""
],
[
"Yoon",
"Youngwoo",
""
],
[
"Ong",
"Hyobin",
""
],
[
"Kim",
"Jaehong",
""
],
[
"Jang",
"Minsu",
""
]
] |
2402.08208 | Mandar Manohar Pitale | Mandar Pitale, Alireza Abbaspour, Devesh Upadhyay | Inherent Diverse Redundant Safety Mechanisms for AI-based Software
Elements in Automotive Applications | This article is accepted for the SAE WCX 2024 conference proceedings | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper explores the role and challenges of Artificial Intelligence (AI)
algorithms, specifically AI-based software elements, in autonomous driving
systems. These AI systems are fundamental in executing real-time critical
functions in complex and high-dimensional environments. They handle vital tasks
like multi-modal perception, cognition, and decision-making tasks such as
motion planning, lane keeping, and emergency braking. A primary concern relates
to the ability (and necessity) of AI models to generalize beyond their initial
training data. This generalization issue becomes evident in real-time
scenarios, where models frequently encounter inputs not represented in their
training or validation data. In such cases, AI systems must still function
effectively despite facing distributional or domain shifts. This paper
investigates the risk associated with overconfident AI models in
safety-critical applications like autonomous driving. To mitigate these risks,
methods for training AI models that help maintain performance without
overconfidence are proposed. This involves implementing certainty reporting
architectures and ensuring diverse training data. While various
distribution-based methods exist to provide safety mechanisms for AI models,
there is a noted lack of systematic assessment of these methods, especially in
the context of safety-critical automotive applications. Many methods in the
literature do not adapt well to the quick response times required in
safety-critical edge applications. This paper reviews these methods, discusses
their suitability for safety-critical applications, and highlights their
strengths and limitations. The paper also proposes potential improvements to
enhance the safety and reliability of AI algorithms in autonomous vehicles in
the context of rapid and accurate decision-making processes.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 04:15:26 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Feb 2024 18:18:04 GMT"
}
] | 1,709,251,200,000 | [
[
"Pitale",
"Mandar",
""
],
[
"Abbaspour",
"Alireza",
""
],
[
"Upadhyay",
"Devesh",
""
]
] |
2402.08211 | Aaron Traylor | Aaron Traylor, Jack Merullo, Michael J. Frank, Ellie Pavlick | Transformer Mechanisms Mimic Frontostriatal Gating Operations When
Trained on Human Working Memory Tasks | 8 pages, 4 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Models based on the Transformer neural network architecture have seen success
on a wide variety of tasks that appear to require complex "cognitive branching"
-- or the ability to maintain pursuit of one goal while accomplishing others.
In cognitive neuroscience, success on such tasks is thought to rely on
sophisticated frontostriatal mechanisms for selective \textit{gating}, which
enable role-addressable updating -- and later readout -- of information to and
from distinct "addresses" of memory, in the form of clusters of neurons.
However, Transformer models have no such mechanisms intentionally built-in. It
is thus an open question how Transformers solve such tasks, and whether the
mechanisms that emerge to help them to do so bear any resemblance to the gating
mechanisms in the human brain. In this work, we analyze the mechanisms that
emerge within a vanilla attention-only Transformer trained on a simple sequence
modeling task inspired by a task explicitly designed to study working memory
gating in computational cognitive neuroscience. We find that, as a result of
training, the self-attention mechanism within the Transformer specializes in a
way that mirrors the input and output gating mechanisms which were explicitly
incorporated into earlier, more biologically-inspired architectures. These
results suggest opportunities for future research on computational similarities
between modern AI architectures and models of the human brain.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 04:28:43 GMT"
}
] | 1,707,868,800,000 | [
[
"Traylor",
"Aaron",
""
],
[
"Merullo",
"Jack",
""
],
[
"Frank",
"Michael J.",
""
],
[
"Pavlick",
"Ellie",
""
]
] |
2402.08236 | Hongyuan Yang | Siqi Peng, Hongyuan Yang, Akihiro Yamamoto | BERT4FCA: A Method for Bipartite Link Prediction using Formal Concept
Analysis and BERT | 23 pages, 5 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We propose BERT4FCA, a novel method for link prediction in bipartite
networks, using formal concept analysis (FCA) and BERT. Link prediction in
bipartite networks is an important task that can solve various practical
problems like friend recommendation in social networks and co-authorship
prediction in author-paper networks. Recent research has found that in
bipartite networks, maximal bi-cliques provide important information for link
prediction, and they can be extracted by FCA. Some FCA-based bipartite link
prediction methods have achieved good performance. However, we figured out that
their performance could be further improved because these methods did not fully
capture the rich information of the extracted maximal bi-cliques. To address
this limitation, we propose an approach using BERT, which can learn more
information from the maximal bi-cliques extracted by FCA and use them to make
link prediction. We conduct experiments on three real-world bipartite networks
and demonstrate that our method outperforms previous FCA-based methods, and
some classic methods such as matrix-factorization and node2vec.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 06:02:05 GMT"
}
] | 1,707,868,800,000 | [
[
"Peng",
"Siqi",
""
],
[
"Yang",
"Hongyuan",
""
],
[
"Yamamoto",
"Akihiro",
""
]
] |
2402.08250 | Yifan Yang | Yifan Yang, Mingquan Lin, Han Zhao, Yifan Peng, Furong Huang, Zhiyong
Lu | A survey of recent methods for addressing AI fairness and bias in
biomedicine | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial intelligence (AI) systems have the potential to revolutionize
clinical practices, including improving diagnostic accuracy and surgical
decision-making, while also reducing costs and manpower. However, it is
important to recognize that these systems may perpetuate social inequities or
demonstrate biases, such as those based on race or gender. Such biases can
occur before, during, or after the development of AI models, making it critical
to understand and address potential biases to enable the accurate and reliable
application of AI models in clinical settings. To mitigate bias concerns during
model development, we surveyed recent publications on different debiasing
methods in the fields of biomedical natural language processing (NLP) or
computer vision (CV). Then we discussed the methods that have been applied in
the biomedical domain to address bias. We performed our literature search on
PubMed, ACM digital library, and IEEE Xplore of relevant articles published
between January 2018 and December 2023 using multiple combinations of keywords.
We then filtered the result of 10,041 articles automatically with loose
constraints, and manually inspected the abstracts of the remaining 890 articles
to identify the 55 articles included in this review. Additional articles in the
references are also included in this review. We discuss each method and compare
its strengths and weaknesses. Finally, we review other potential methods from
the general domain that could be applied to biomedicine to address bias and
improve fairness.The bias of AIs in biomedicine can originate from multiple
sources. Existing debiasing methods that focus on algorithms can be categorized
into distributional or algorithmic.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 06:38:46 GMT"
}
] | 1,707,868,800,000 | [
[
"Yang",
"Yifan",
""
],
[
"Lin",
"Mingquan",
""
],
[
"Zhao",
"Han",
""
],
[
"Peng",
"Yifan",
""
],
[
"Huang",
"Furong",
""
],
[
"Lu",
"Zhiyong",
""
]
] |
2402.08284 | Takanori Ugai | Takanori Ugai, Yusuke Koyanagi, Fumihito Nishino | A Logical Approach to Criminal Case Investigation | 11 pages, 11 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | XAI (eXplanable AI) techniques that have the property of explaining the
reasons for their conclusions, i.e. explainability or interpretability, are
attracting attention. XAI is expected to be used in the development of forensic
science and the justice system. In today's forensic and criminal investigation
environment, experts face many challenges due to large amounts of data, small
pieces of evidence in a chaotic and complex environment, traditional laboratory
structures and sometimes inadequate knowledge. All these can lead to failed
investigations and miscarriages of justice. In this paper, we describe the
application of one logical approach to crime scene investigation. The subject
of the application is ``The Adventure of the Speckled Band'' from the Sherlock
Holmes short stories. The applied data is the knowledge graph created for the
Knowledge Graph Reasoning Challenge. We tried to find the murderer by inferring
each person with the motive, opportunity, and method. We created an ontology of
motives and methods of murder from dictionaries and dictionaries, added it to
the knowledge graph of ``The Adventure of the Speckled Band'', and applied
scripts to determine motives, opportunities, and methods.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 08:24:32 GMT"
}
] | 1,707,868,800,000 | [
[
"Ugai",
"Takanori",
""
],
[
"Koyanagi",
"Yusuke",
""
],
[
"Nishino",
"Fumihito",
""
]
] |
2402.08298 | Josu Ceberio | Josu Ceberio, Borja Calvo | Time to Stop and Think: What kind of research do we want to do? | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Experimentation is an intrinsic part of research in artificial intelligence
since it allows for collecting quantitative observations, validating
hypotheses, and providing evidence for their reformulation. For that reason,
experimentation must be coherent with the purposes of the research, properly
addressing the relevant questions in each case. Unfortunately, the literature
is full of works whose experimentation is neither rigorous nor convincing,
oftentimes designed to support prior beliefs rather than answering the relevant
research questions.
In this paper, we focus on the field of metaheuristic optimization, since it
is our main field of work, and it is where we have observed the misconduct that
has motivated this letter. Even if we limit the focus of this manuscript to the
experimental part of the research, our main goal is to sew the seed of sincere
critical assessment of our work, sparking a reflection process both at the
individual and the community level. Such a reflection process is too complex
and extensive to be tackled as a whole. Therefore, to bring our feet to the
ground, we will include in this document our reflections about the role of
experimentation in our work, discussing topics such as the use of benchmark
instances vs instance generators, or the statistical assessment of empirical
results. That is, all the statements included in this document are personal
views and opinions, which can be shared by others or not. Certainly, having
different points of view is the basis to establish a good discussion process.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 08:53:57 GMT"
}
] | 1,707,868,800,000 | [
[
"Ceberio",
"Josu",
""
],
[
"Calvo",
"Borja",
""
]
] |
2402.08369 | Sangwoo Shin | Sangwoo Shin, Daehee Lee, Minjong Yoo, Woo Kyung Kim, Honguk Woo | One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill | ICML-2023 Camera Ready Version | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | One-shot imitation is to learn a new task from a single demonstration, yet it
is a challenging problem to adopt it for complex tasks with the high domain
diversity inherent in a non-stationary environment. To tackle the problem, we
explore the compositionality of complex tasks, and present a novel skill-based
imitation learning framework enabling one-shot imitation and zero-shot
adaptation; from a single demonstration for a complex unseen task, a semantic
skill sequence is inferred and then each skill in the sequence is converted
into an action sequence optimized for environmental hidden dynamics that can
vary over time. Specifically, we leverage a vision-language model to learn a
semantic skill set from offline video datasets, where each skill is represented
on the vision-language embedding space, and adapt meta-learning with dynamics
inference to enable zero-shot skill adaptation. We evaluate our framework with
various one-shot imitation scenarios for extended multi-stage Meta-world tasks,
showing its superiority in learning complex tasks, generalizing to dynamics
changes, and extending to different demonstration conditions and modalities,
compared to other baselines.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 11:01:52 GMT"
}
] | 1,707,868,800,000 | [
[
"Shin",
"Sangwoo",
""
],
[
"Lee",
"Daehee",
""
],
[
"Yoo",
"Minjong",
""
],
[
"Kim",
"Woo Kyung",
""
],
[
"Woo",
"Honguk",
""
]
] |
2402.08423 | Jianwu Fang | Peining Shen, Jianwu Fang, Hongkai Yu, and Jianru Xue | Vehicle Behavior Prediction by Episodic-Memory Implanted NDT | Accepted by ICRA2024 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In autonomous driving, predicting the behavior (turning left, stopping, etc.)
of target vehicles is crucial for the self-driving vehicle to make safe
decisions and avoid accidents. Existing deep learning-based methods have shown
excellent and accurate performance, but the black-box nature makes it
untrustworthy to apply them in practical use. In this work, we explore the
interpretability of behavior prediction of target vehicles by an Episodic
Memory implanted Neural Decision Tree (abbrev. eMem-NDT). The structure of
eMem-NDT is constructed by hierarchically clustering the text embedding of
vehicle behavior descriptions. eMem-NDT is a neural-backed part of a
pre-trained deep learning model by changing the soft-max layer of the deep
model to eMem-NDT, for grouping and aligning the memory prototypes of the
historical vehicle behavior features in training data on a neural decision
tree. Each leaf node of eMem-NDT is modeled by a neural network for aligning
the behavior memory prototypes. By eMem-NDT, we infer each instance in behavior
prediction of vehicles by bottom-up Memory Prototype Matching (MPM) (searching
the appropriate leaf node and the links to the root node) and top-down Leaf
Link Aggregation (LLA) (obtaining the probability of future behaviors of
vehicles for certain instances). We validate eMem-NDT on BLVD and LOKI
datasets, and the results show that our model can obtain a superior performance
to other methods with clear explainability. The code is available at
https://github.com/JWFangit/eMem-NDT.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 12:50:04 GMT"
}
] | 1,707,868,800,000 | [
[
"Shen",
"Peining",
""
],
[
"Fang",
"Jianwu",
""
],
[
"Yu",
"Hongkai",
""
],
[
"Xue",
"Jianru",
""
]
] |
2402.08472 | Christian Blum | Camilo Chac\'on Sartori and Christian Blum and Gabriela Ochoa | Large Language Models for the Automated Analysis of Optimization
Algorithms | Submitted to the GECCO 2024 conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability of Large Language Models (LLMs) to generate high-quality text and
code has fuelled their rise in popularity. In this paper, we aim to demonstrate
the potential of LLMs within the realm of optimization algorithms by
integrating them into STNWeb. This is a web-based tool for the generation of
Search Trajectory Networks (STNs), which are visualizations of optimization
algorithm behavior. Although visualizations produced by STNWeb can be very
informative for algorithm designers, they often require a certain level of
prior knowledge to be interpreted. In an attempt to bridge this knowledge gap,
we have incorporated LLMs, specifically GPT-4, into STNWeb to produce extensive
written reports, complemented by automatically generated plots, thereby
enhancing the user experience and reducing the barriers to the adoption of this
tool by the research community. Moreover, our approach can be expanded to other
tools from the optimization community, showcasing the versatility and potential
of LLMs in this field.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 14:05:02 GMT"
}
] | 1,707,868,800,000 | [
[
"Sartori",
"Camilo Chacón",
""
],
[
"Blum",
"Christian",
""
],
[
"Ochoa",
"Gabriela",
""
]
] |
2402.08492 | Xiaobo Liu | Xiaoqiang Liu, Yubin Wang, Zicheng Huang, Boming Xu, Yilin Zeng, Xinqi
Chen, Zilong Wang, Enning Yang, Xiaoxuan Lei, Yisen Huang, Xiaobo Liu | The Application of ChatGPT in Responding to Questions Related to the
Boston Bowel Preparation Scale | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Background: Colonoscopy, a crucial diagnostic tool in gastroenterology,
depends heavily on superior bowel preparation. ChatGPT, a large language model
with emergent intelligence which also exhibits potential in medical
applications. This study aims to assess the accuracy and consistency of ChatGPT
in using the Boston Bowel Preparation Scale (BBPS) for colonoscopy assessment.
Methods: We retrospectively collected 233 colonoscopy images from 2020 to 2023.
These images were evaluated using the BBPS by 3 senior endoscopists and 3
novice endoscopists. Additionally, ChatGPT also assessed these images, having
been divided into three groups and undergone specific Fine-tuning. Consistency
was evaluated through two rounds of testing. Results: In the initial round,
ChatGPT's accuracy varied between 48.93% and 62.66%, trailing the endoscopists'
accuracy of 76.68% to 77.83%. Kappa values for ChatGPT was between 0.52 and
0.53, compared to 0.75 to 0.87 for the endoscopists. Conclusion: While ChatGPT
shows promise in bowel preparation scoring, it currently does not match the
accuracy and consistency of experienced endoscopists. Future research should
focus on in-depth Fine-tuning.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 14:38:12 GMT"
}
] | 1,707,868,800,000 | [
[
"Liu",
"Xiaoqiang",
""
],
[
"Wang",
"Yubin",
""
],
[
"Huang",
"Zicheng",
""
],
[
"Xu",
"Boming",
""
],
[
"Zeng",
"Yilin",
""
],
[
"Chen",
"Xinqi",
""
],
[
"Wang",
"Zilong",
""
],
[
"Yang",
"Enning",
""
],
[
"Lei",
"Xiaoxuan",
""
],
[
"Huang",
"Yisen",
""
],
[
"Liu",
"Xiaobo",
""
]
] |
2402.08511 | Cedric Derstroff | Cedric Derstroff, Jannis Brugger, Jannis Bl\"uml, Mira Mezini, Stefan
Kramer, Kristian Kersting | Amplifying Exploration in Monte-Carlo Tree Search by Focusing on the
Unknown | 10 pages, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Monte-Carlo tree search (MCTS) is an effective anytime algorithm with a vast
amount of applications. It strategically allocates computational resources to
focus on promising segments of the search tree, making it a very attractive
search algorithm in large search spaces. However, it often expends its limited
resources on reevaluating previously explored regions when they remain the most
promising path. Our proposed methodology, denoted as AmEx-MCTS, solves this
problem by introducing a novel MCTS formulation. Central to AmEx-MCTS is the
decoupling of value updates, visit count updates, and the selected path during
the tree search, thereby enabling the exclusion of already explored subtrees or
leaves. This segregation preserves the utility of visit counts for both
exploration-exploitation balancing and quality metrics within MCTS. The
resultant augmentation facilitates in a considerably broader search using
identical computational resources, preserving the essential characteristics of
MCTS. The expanded coverage not only yields more precise estimations but also
proves instrumental in larger and more complex problems. Our empirical
evaluation demonstrates the superior performance of AmEx-MCTS, surpassing
classical MCTS and related approaches by a substantial margin.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 15:05:54 GMT"
}
] | 1,707,868,800,000 | [
[
"Derstroff",
"Cedric",
""
],
[
"Brugger",
"Jannis",
""
],
[
"Blüml",
"Jannis",
""
],
[
"Mezini",
"Mira",
""
],
[
"Kramer",
"Stefan",
""
],
[
"Kersting",
"Kristian",
""
]
] |
2402.08514 | Milad Kazemi | Milad Kazemi, Jessica Lally, Ekaterina Tishchenko, Hana Chockler and
Nicola Paoletti | Counterfactual Influence in Markov Decision Processes | 12 pages, 6 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Our work addresses a fundamental problem in the context of counterfactual
inference for Markov Decision Processes (MDPs). Given an MDP path $\tau$, this
kind of inference allows us to derive counterfactual paths $\tau'$ describing
what-if versions of $\tau$ obtained under different action sequences than those
observed in $\tau$. However, as the counterfactual states and actions deviate
from the observed ones over time, the observation $\tau$ may no longer
influence the counterfactual world, meaning that the analysis is no longer
tailored to the individual observation, resulting in interventional outcomes
rather than counterfactual ones. Even though this issue specifically affects
the popular Gumbel-max structural causal model used for MDP counterfactuals, it
has remained overlooked until now. In this work, we introduce a formal
characterisation of influence based on comparing counterfactual and
interventional distributions. We devise an algorithm to construct
counterfactual models that automatically satisfy influence constraints.
Leveraging such models, we derive counterfactual policies that are not just
optimal for a given reward structure but also remain tailored to the observed
path. Even though there is an unavoidable trade-off between policy optimality
and strength of influence constraints, our experiments demonstrate that it is
possible to derive (near-)optimal policies while remaining under the influence
of the observation.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 15:10:30 GMT"
}
] | 1,707,868,800,000 | [
[
"Kazemi",
"Milad",
""
],
[
"Lally",
"Jessica",
""
],
[
"Tishchenko",
"Ekaterina",
""
],
[
"Chockler",
"Hana",
""
],
[
"Paoletti",
"Nicola",
""
]
] |
2402.08646 | Hiroyuki Kido | Hiroyuki Kido | Inference of Abstraction for a Unified Account of Symbolic Reasoning
from Data | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inspired by empirical work in neuroscience for Bayesian approaches to brain
function, we give a unified probabilistic account of various types of symbolic
reasoning from data. We characterise them in terms of formal logic using the
classical consequence relation, an empirical consequence relation, maximal
consistent sets, maximal possible sets and maximum likelihood estimation. The
theory gives new insights into reasoning towards human-like machine
intelligence.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 18:24:23 GMT"
}
] | 1,707,868,800,000 | [
[
"Kido",
"Hiroyuki",
""
]
] |
2402.08670 | Yu Wang | Yuqing Liu, Yu Wang, Lichao Sun, Philip S. Yu | Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models | under review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The development of large vision-language models (LVLMs) offers the potential
to address challenges faced by traditional multimodal recommendations thanks to
their proficient understanding of static images and textual dynamics. However,
the application of LVLMs in this field is still limited due to the following
complexities: First, LVLMs lack user preference knowledge as they are trained
from vast general datasets. Second, LVLMs suffer setbacks in addressing
multiple image dynamics in scenarios involving discrete, noisy, and redundant
image sequences. To overcome these issues, we propose the novel reasoning
scheme named Rec-GPT4V: Visual-Summary Thought (VST) of leveraging large
vision-language models for multimodal recommendation. We utilize user history
as in-context user preferences to address the first challenge. Next, we prompt
LVLMs to generate item image summaries and utilize image comprehension in
natural language space combined with item titles to query the user preferences
over candidate items. We conduct comprehensive experiments across four datasets
with three LVLMs: GPT4-V, LLaVa-7b, and LLaVa-13b. The numerical results
indicate the efficacy of VST.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 18:51:18 GMT"
}
] | 1,707,868,800,000 | [
[
"Liu",
"Yuqing",
""
],
[
"Wang",
"Yu",
""
],
[
"Sun",
"Lichao",
""
],
[
"Yu",
"Philip S.",
""
]
] |
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