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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2304.02187 | Chenyan Wu | Chenyan Wu, Dolzodmaa Davaasuren, Tal Shafir, Rachelle Tsachor, James
Z. Wang | Bodily expressed emotion understanding through integrating Laban
movement analysis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Body movements carry important information about a person's emotions or
mental state and are essential in daily communication. Enhancing the ability of
machines to understand emotions expressed through body language can improve the
communication of assistive robots with children and elderly users, provide
psychiatric professionals with quantitative diagnostic and prognostic
assistance, and aid law enforcement in identifying deception. This study
develops a high-quality human motor element dataset based on the Laban Movement
Analysis movement coding system and utilizes that to jointly learn about motor
elements and emotions. Our long-term ambition is to integrate knowledge from
computing, psychology, and performing arts to enable automated understanding
and analysis of emotion and mental state through body language. This work
serves as a launchpad for further research into recognizing emotions through
analysis of human movement.
| [
{
"version": "v1",
"created": "Wed, 5 Apr 2023 02:07:15 GMT"
}
] | 1,680,739,200,000 | [
[
"Wu",
"Chenyan",
""
],
[
"Davaasuren",
"Dolzodmaa",
""
],
[
"Shafir",
"Tal",
""
],
[
"Tsachor",
"Rachelle",
""
],
[
"Wang",
"James Z.",
""
]
] |
2304.02653 | Neelesh Mungoli | Neelesh Mungoli | Adaptive Ensemble Learning: Boosting Model Performance through
Intelligent Feature Fusion in Deep Neural Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present an Adaptive Ensemble Learning framework that aims
to boost the performance of deep neural networks by intelligently fusing
features through ensemble learning techniques. The proposed framework
integrates ensemble learning strategies with deep learning architectures to
create a more robust and adaptable model capable of handling complex tasks
across various domains. By leveraging intelligent feature fusion methods, the
Adaptive Ensemble Learning framework generates more discriminative and
effective feature representations, leading to improved model performance and
generalization capabilities.
We conducted extensive experiments and evaluations on several benchmark
datasets, including image classification, object detection, natural language
processing, and graph-based learning tasks. The results demonstrate that the
proposed framework consistently outperforms baseline models and traditional
feature fusion techniques, highlighting its effectiveness in enhancing deep
learning models' performance. Furthermore, we provide insights into the impact
of intelligent feature fusion on model performance and discuss the potential
applications of the Adaptive Ensemble Learning framework in real-world
scenarios.
The paper also explores the design and implementation of adaptive ensemble
models, ensemble training strategies, and meta-learning techniques, which
contribute to the framework's versatility and adaptability. In conclusion, the
Adaptive Ensemble Learning framework represents a significant advancement in
the field of feature fusion and ensemble learning for deep neural networks,
with the potential to transform a wide range of applications across multiple
domains.
| [
{
"version": "v1",
"created": "Tue, 4 Apr 2023 21:49:49 GMT"
}
] | 1,680,825,600,000 | [
[
"Mungoli",
"Neelesh",
""
]
] |
2304.02769 | Viswanath Chadalapaka | Viswanath Chadalapaka, Derek Nguyen, JoonWon Choi, Shaunak Joshi,
Mohammad Rostami | Low-Shot Learning for Fictional Claim Verification | 6 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we study the problem of claim verification in the context of
claims about fictional stories in a low-shot learning setting. To this end, we
generate two synthetic datasets and then develop an end-to-end pipeline and
model that is tested on both benchmarks. To test the efficacy of our pipeline
and the difficulty of benchmarks, we compare our models' results against human
and random assignment results. Our code is available at
https://github.com/Derposoft/plot_hole_detection.
| [
{
"version": "v1",
"created": "Wed, 5 Apr 2023 22:20:40 GMT"
}
] | 1,680,825,600,000 | [
[
"Chadalapaka",
"Viswanath",
""
],
[
"Nguyen",
"Derek",
""
],
[
"Choi",
"JoonWon",
""
],
[
"Joshi",
"Shaunak",
""
],
[
"Rostami",
"Mohammad",
""
]
] |
2304.02924 | Yingbo Li | Yingbo Li, Anamaria-Beatrice Spulber, Yucong Duan | The Governance of Physical Artificial Intelligence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Physical artificial intelligence can prove to be one of the most important
challenges of the artificial intelligence. The governance of physical
artificial intelligence would define its responsible intelligent application in
the society.
| [
{
"version": "v1",
"created": "Thu, 6 Apr 2023 08:26:38 GMT"
}
] | 1,680,825,600,000 | [
[
"Li",
"Yingbo",
""
],
[
"Spulber",
"Anamaria-Beatrice",
""
],
[
"Duan",
"Yucong",
""
]
] |
2304.03031 | Yongho Song | Yongho Song, Dahyun Lee, Myungha Jang, Seung-won Hwang, Kyungjae Lee,
Dongha Lee, Jinyeong Yeo | Evidentiality-aware Retrieval for Overcoming Abstractiveness in
Open-Domain Question Answering | Findings of EACL 2024 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The long-standing goal of dense retrievers in abtractive open-domain question
answering (ODQA) tasks is to learn to capture evidence passages among relevant
passages for any given query, such that the reader produce factually correct
outputs from evidence passages. One of the key challenge is the insufficient
amount of training data with the supervision of the answerability of the
passages. Recent studies rely on iterative pipelines to annotate answerability
using signals from the reader, but their high computational costs hamper
practical applications. In this paper, we instead focus on a data-centric
approach and propose Evidentiality-Aware Dense Passage Retrieval (EADPR), which
leverages synthetic distractor samples to learn to discriminate evidence
passages from distractors. We conduct extensive experiments to validate the
effectiveness of our proposed method on multiple abstractive ODQA tasks.
| [
{
"version": "v1",
"created": "Thu, 6 Apr 2023 12:42:37 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Apr 2023 05:20:12 GMT"
},
{
"version": "v3",
"created": "Tue, 11 Apr 2023 08:15:15 GMT"
},
{
"version": "v4",
"created": "Wed, 12 Apr 2023 11:09:59 GMT"
},
{
"version": "v5",
"created": "Thu, 4 May 2023 06:40:20 GMT"
},
{
"version": "v6",
"created": "Thu, 1 Feb 2024 17:36:39 GMT"
}
] | 1,706,832,000,000 | [
[
"Song",
"Yongho",
""
],
[
"Lee",
"Dahyun",
""
],
[
"Jang",
"Myungha",
""
],
[
"Hwang",
"Seung-won",
""
],
[
"Lee",
"Kyungjae",
""
],
[
"Lee",
"Dongha",
""
],
[
"Yeo",
"Jinyeong",
""
]
] |
2304.03060 | Konrad Kulakowski | Jacek Szybowski and Konrad Ku{\l}akowski and Sebastian Ernst | Almost optimal manipulation of a pair of alternatives | 18 pages | Szybowski, J., Ku{\l}akowski, K. & Ernst, S. Almost optimal
manipulation of pairwise comparisons of alternatives. J Glob Optim (2024) | 10.1007/s10898-024-01391-3 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The role of an expert in the decision-making process is crucial, as the final
recommendation depends on his disposition, clarity of mind, experience, and
knowledge of the problem. However, the recommendation also depends on their
honesty. But what if the expert is dishonest? Then, the answer on how difficult
it is to manipulate in a given case becomes essential. In the presented work,
we consider manipulation of a ranking obtained by comparing alternatives in
pairs. More specifically, we propose an algorithm for finding an almost optimal
way to swap the positions of two selected alternatives. Thanks to this, it is
possible to determine how difficult such manipulation is in a given case.
Theoretical considerations are illustrated by a practical example.
| [
{
"version": "v1",
"created": "Thu, 6 Apr 2023 13:24:32 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Apr 2023 16:41:19 GMT"
}
] | 1,716,854,400,000 | [
[
"Szybowski",
"Jacek",
""
],
[
"Kułakowski",
"Konrad",
""
],
[
"Ernst",
"Sebastian",
""
]
] |
2304.03103 | Mirza Mohtashim Alam | Karishma Mohiuddin, Mirza Ariful Alam, Mirza Mohtashim Alam, Pascal
Welke, Michael Martin, Jens Lehmann, Sahar Vahdati | Retention Is All You Need | Accepted at CIKM 2023 Applied Research Track | null | 10.1145/3583780.3615497 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Skilled employees are the most important pillars of an organization. Despite
this, most organizations face high attrition and turnover rates. While several
machine learning models have been developed to analyze attrition and its causal
factors, the interpretations of those models remain opaque. In this paper, we
propose the HR-DSS approach, which stands for Human Resource (HR) Decision
Support System, and uses explainable AI for employee attrition problems. The
system is designed to assist HR departments in interpreting the predictions
provided by machine learning models. In our experiments, we employ eight
machine learning models to provide predictions. We further process the results
achieved by the best-performing model by the SHAP explainability process and
use the SHAP values to generate natural language explanations which can be
valuable for HR. Furthermore, using "What-if-analysis", we aim to observe
plausible causes for attrition of an individual employee. The results show that
by adjusting the specific dominant features of each individual, employee
attrition can turn into employee retention through informative business
decisions.
| [
{
"version": "v1",
"created": "Thu, 6 Apr 2023 14:29:20 GMT"
},
{
"version": "v2",
"created": "Sat, 26 Aug 2023 21:06:13 GMT"
}
] | 1,693,267,200,000 | [
[
"Mohiuddin",
"Karishma",
""
],
[
"Alam",
"Mirza Ariful",
""
],
[
"Alam",
"Mirza Mohtashim",
""
],
[
"Welke",
"Pascal",
""
],
[
"Martin",
"Michael",
""
],
[
"Lehmann",
"Jens",
""
],
[
"Vahdati",
"Sahar",
""
]
] |
2304.03262 | Jiuhai Chen | Jiuhai Chen, Lichang Chen, Heng Huang, Tianyi Zhou | When do you need Chain-of-Thought Prompting for ChatGPT? | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Chain-of-Thought (CoT) prompting can effectively elicit complex multi-step
reasoning from Large Language Models~(LLMs). For example, by simply adding CoT
instruction ``Let's think step-by-step'' to each input query of MultiArith
dataset, GPT-3's accuracy can be improved from 17.7\% to 78.7\%. However, it is
not clear whether CoT is still effective on more recent instruction finetuned
(IFT) LLMs such as ChatGPT. Surprisingly, on ChatGPT, CoT is no longer
effective for certain tasks such as arithmetic reasoning while still keeping
effective on other reasoning tasks. Moreover, on the former tasks, ChatGPT
usually achieves the best performance and can generate CoT even without being
instructed to do so. Hence, it is plausible that ChatGPT has already been
trained on these tasks with CoT and thus memorized the instruction so it
implicitly follows such an instruction when applied to the same queries, even
without CoT. Our analysis reflects a potential risk of overfitting/bias toward
instructions introduced in IFT, which becomes more common in training LLMs. In
addition, it indicates possible leakage of the pretraining recipe, e.g., one
can verify whether a dataset and instruction were used in training ChatGPT. Our
experiments report new baseline results of ChatGPT on a variety of reasoning
tasks and shed novel insights into LLM's profiling, instruction memorization,
and pretraining dataset leakage.
| [
{
"version": "v1",
"created": "Thu, 6 Apr 2023 17:47:29 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Apr 2023 14:45:18 GMT"
}
] | 1,681,862,400,000 | [
[
"Chen",
"Jiuhai",
""
],
[
"Chen",
"Lichang",
""
],
[
"Huang",
"Heng",
""
],
[
"Zhou",
"Tianyi",
""
]
] |
2304.03375 | Gilles Falquet | Sahar Aljalbout, Gilles Falquet, Didier Buchs | Handling Wikidata Qualifiers in Reasoning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Wikidata is a knowledge graph increasingly adopted by many communities for
diverse applications. Wikidata statements are annotated with qualifier-value
pairs that are used to depict information, such as the validity context of the
statement, its causality, provenances, etc. Handling the qualifiers in
reasoning is a challenging problem. When defining inference rules (in
particular, rules on ontological properties (x subclass of y, z instance of x,
etc.)), one must consider the qualifiers, as most of them participate in the
semantics of the statements. This poses a complex problem because a) there is a
massive number of qualifiers, and b) the qualifiers of the inferred statement
are often a combination of the qualifiers in the rule condition. In this work,
we propose to address this problem by a) defining a categorization of the
qualifiers b) formalizing the Wikidata model with a many-sorted logical
language; the sorts of this language are the qualifier categories. We couple
this logic with an algebraic specification that provides a means for
effectively handling qualifiers in inference rules. Using Wikidata ontological
properties, we show how to use the MSL and specification to reason on
qualifiers. Finally, we discuss the methodology for practically implementing
the work and present a prototype implementation. The work can be naturally
extended, thanks to the extensibility of the many-sorted algebraic
specification, to cover more qualifiers in the specification, such as uncertain
time, recurring events, geographic locations, and others.
| [
{
"version": "v1",
"created": "Thu, 6 Apr 2023 21:05:52 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Jun 2023 13:12:56 GMT"
}
] | 1,687,392,000,000 | [
[
"Aljalbout",
"Sahar",
""
],
[
"Falquet",
"Gilles",
""
],
[
"Buchs",
"Didier",
""
]
] |
2304.04640 | Jason Yik | Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes
Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson,
Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan
Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag
Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian
Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan,
Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico
Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit
Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve
Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez,
Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C.
Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong
Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taul\'e,
Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas
Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil
Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro,
Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer,
Andr\'e van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman,
Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos
Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp
Stratmann, Jonathan Timcheck, Nergis T\"omen, Gianvito Urgese, Marian
Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima
Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi | NeuroBench: A Framework for Benchmarking Neuromorphic Computing
Algorithms and Systems | Updated from whitepaper to full perspective article preprint | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Neuromorphic computing shows promise for advancing computing efficiency and
capabilities of AI applications using brain-inspired principles. However, the
neuromorphic research field currently lacks standardized benchmarks, making it
difficult to accurately measure technological advancements, compare performance
with conventional methods, and identify promising future research directions.
Prior neuromorphic computing benchmark efforts have not seen widespread
adoption due to a lack of inclusive, actionable, and iterative benchmark design
and guidelines. To address these shortcomings, we present NeuroBench: a
benchmark framework for neuromorphic computing algorithms and systems.
NeuroBench is a collaboratively-designed effort from an open community of
nearly 100 co-authors across over 50 institutions in industry and academia,
aiming to provide a representative structure for standardizing the evaluation
of neuromorphic approaches. The NeuroBench framework introduces a common set of
tools and systematic methodology for inclusive benchmark measurement,
delivering an objective reference framework for quantifying neuromorphic
approaches in both hardware-independent (algorithm track) and
hardware-dependent (system track) settings. In this article, we present initial
performance baselines across various model architectures on the algorithm track
and outline the system track benchmark tasks and guidelines. NeuroBench is
intended to continually expand its benchmarks and features to foster and track
the progress made by the research community.
| [
{
"version": "v1",
"created": "Mon, 10 Apr 2023 15:12:09 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Apr 2023 20:36:13 GMT"
},
{
"version": "v3",
"created": "Wed, 17 Jan 2024 20:40:28 GMT"
}
] | 1,705,622,400,000 | [
[
"Yik",
"Jason",
""
],
[
"Berghe",
"Korneel Van den",
""
],
[
"Blanken",
"Douwe den",
""
],
[
"Bouhadjar",
"Younes",
""
],
[
"Fabre",
"Maxime",
""
],
[
"Hueber",
"Paul",
""
],
[
"Kleyko",
"Denis",
""
],
[
"Pacik-Nelson",
"Noah",
""
],
[
"Sun",
"Pao-Sheng Vincent",
""
],
[
"Tang",
"Guangzhi",
""
],
[
"Wang",
"Shenqi",
""
],
[
"Zhou",
"Biyan",
""
],
[
"Ahmed",
"Soikat Hasan",
""
],
[
"Joseph",
"George Vathakkattil",
""
],
[
"Leto",
"Benedetto",
""
],
[
"Micheli",
"Aurora",
""
],
[
"Mishra",
"Anurag Kumar",
""
],
[
"Lenz",
"Gregor",
""
],
[
"Sun",
"Tao",
""
],
[
"Ahmed",
"Zergham",
""
],
[
"Akl",
"Mahmoud",
""
],
[
"Anderson",
"Brian",
""
],
[
"Andreou",
"Andreas G.",
""
],
[
"Bartolozzi",
"Chiara",
""
],
[
"Basu",
"Arindam",
""
],
[
"Bogdan",
"Petrut",
""
],
[
"Bohte",
"Sander",
""
],
[
"Buckley",
"Sonia",
""
],
[
"Cauwenberghs",
"Gert",
""
],
[
"Chicca",
"Elisabetta",
""
],
[
"Corradi",
"Federico",
""
],
[
"de Croon",
"Guido",
""
],
[
"Danielescu",
"Andreea",
""
],
[
"Daram",
"Anurag",
""
],
[
"Davies",
"Mike",
""
],
[
"Demirag",
"Yigit",
""
],
[
"Eshraghian",
"Jason",
""
],
[
"Fischer",
"Tobias",
""
],
[
"Forest",
"Jeremy",
""
],
[
"Fra",
"Vittorio",
""
],
[
"Furber",
"Steve",
""
],
[
"Furlong",
"P. Michael",
""
],
[
"Gilpin",
"William",
""
],
[
"Gilra",
"Aditya",
""
],
[
"Gonzalez",
"Hector A.",
""
],
[
"Indiveri",
"Giacomo",
""
],
[
"Joshi",
"Siddharth",
""
],
[
"Karia",
"Vedant",
""
],
[
"Khacef",
"Lyes",
""
],
[
"Knight",
"James C.",
""
],
[
"Kriener",
"Laura",
""
],
[
"Kubendran",
"Rajkumar",
""
],
[
"Kudithipudi",
"Dhireesha",
""
],
[
"Liu",
"Yao-Hong",
""
],
[
"Liu",
"Shih-Chii",
""
],
[
"Ma",
"Haoyuan",
""
],
[
"Manohar",
"Rajit",
""
],
[
"Margarit-Taulé",
"Josep Maria",
""
],
[
"Mayr",
"Christian",
""
],
[
"Michmizos",
"Konstantinos",
""
],
[
"Muir",
"Dylan",
""
],
[
"Neftci",
"Emre",
""
],
[
"Nowotny",
"Thomas",
""
],
[
"Ottati",
"Fabrizio",
""
],
[
"Ozcelikkale",
"Ayca",
""
],
[
"Panda",
"Priyadarshini",
""
],
[
"Park",
"Jongkil",
""
],
[
"Payvand",
"Melika",
""
],
[
"Pehle",
"Christian",
""
],
[
"Petrovici",
"Mihai A.",
""
],
[
"Pierro",
"Alessandro",
""
],
[
"Posch",
"Christoph",
""
],
[
"Renner",
"Alpha",
""
],
[
"Sandamirskaya",
"Yulia",
""
],
[
"Schaefer",
"Clemens JS",
""
],
[
"van Schaik",
"André",
""
],
[
"Schemmel",
"Johannes",
""
],
[
"Schmidgall",
"Samuel",
""
],
[
"Schuman",
"Catherine",
""
],
[
"Seo",
"Jae-sun",
""
],
[
"Sheik",
"Sadique",
""
],
[
"Shrestha",
"Sumit Bam",
""
],
[
"Sifalakis",
"Manolis",
""
],
[
"Sironi",
"Amos",
""
],
[
"Stewart",
"Matthew",
""
],
[
"Stewart",
"Kenneth",
""
],
[
"Stewart",
"Terrence C.",
""
],
[
"Stratmann",
"Philipp",
""
],
[
"Timcheck",
"Jonathan",
""
],
[
"Tömen",
"Nergis",
""
],
[
"Urgese",
"Gianvito",
""
],
[
"Verhelst",
"Marian",
""
],
[
"Vineyard",
"Craig M.",
""
],
[
"Vogginger",
"Bernhard",
""
],
[
"Yousefzadeh",
"Amirreza",
""
],
[
"Zohora",
"Fatima Tuz",
""
],
[
"Frenkel",
"Charlotte",
""
],
[
"Reddi",
"Vijay Janapa",
""
]
] |
2304.04751 | Opeoluwa Owoyele | Eloghosa Ikponmwoba and Ope Owoyele | DeepHive: A multi-agent reinforcement learning approach for automated
discovery of swarm-based optimization policies | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an approach for designing swarm-based optimizers for the global
optimization of expensive black-box functions. In the proposed approach, the
problem of finding efficient optimizers is framed as a reinforcement learning
problem, where the goal is to find optimization policies that require a few
function evaluations to converge to the global optimum. The state of each agent
within the swarm is defined as its current position and function value within a
design space and the agents learn to take favorable actions that maximize
reward, which is based on the final value of the objective function. The
proposed approach is tested on various benchmark optimization functions and
compared to the performance of other global optimization strategies.
Furthermore, the effect of changing the number of agents, as well as the
generalization capabilities of the trained agents are investigated. The results
show superior performance compared to the other optimizers, desired scaling
when the number of agents is varied, and acceptable performance even when
applied to unseen functions. On a broader scale, the results show promise for
the rapid development of domain-specific optimizers.
| [
{
"version": "v1",
"created": "Wed, 29 Mar 2023 18:08:08 GMT"
}
] | 1,681,257,600,000 | [
[
"Ikponmwoba",
"Eloghosa",
""
],
[
"Owoyele",
"Ope",
""
]
] |
2304.04893 | Yanlin Qi | Yanlin Qi, Gengchen Mai, Rui Zhu, and Michael Zhang | EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph
for Smart Transportation System | null | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Over the past decade, the electric vehicle industry has experienced
unprecedented growth and diversification, resulting in a complex ecosystem. To
effectively manage this multifaceted field, we present an EV-centric knowledge
graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial
knowledge management system. The EVKG encapsulates essential EV-related
knowledge, including EV adoption, electric vehicle supply equipment, and
electricity transmission network, to support decision-making related to EV
technology development, infrastructure planning, and policy-making by providing
timely and accurate information and analysis. To enrich and contextualize the
EVKG, we integrate the developed EV-relevant ontology modules from existing
well-known knowledge graphs and ontologies. This integration enables
interoperability with other knowledge graphs in the Linked Data Open Cloud,
enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six
competency questions, we demonstrate how the EVKG can be used to answer various
types of EV-related questions, providing critical insights into the EV
ecosystem. Our EVKG provides an efficient and effective approach for managing
the complex and diverse EV industry. By consolidating critical EV-related
knowledge into a single, easily accessible resource, the EVKG supports
decision-makers in making informed choices about EV technology development,
infrastructure planning, and policy-making. As a flexible and extensible
platform, the EVKG is capable of accommodating a wide range of data sources,
enabling it to evolve alongside the rapidly changing EV landscape.
| [
{
"version": "v1",
"created": "Mon, 10 Apr 2023 23:01:02 GMT"
}
] | 1,681,257,600,000 | [
[
"Qi",
"Yanlin",
""
],
[
"Mai",
"Gengchen",
""
],
[
"Zhu",
"Rui",
""
],
[
"Zhang",
"Michael",
""
]
] |
2304.05077 | Johannes Kleiner | Johannes Kleiner, Tim Ludwig | If consciousness is dynamically relevant, artificial intelligence isn't
conscious | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We demonstrate that if consciousness is relevant for the temporal evolution
of a system's states--that is, if it is dynamically relevant--then AI systems
cannot be conscious. That is because AI systems run on CPUs, GPUs, TPUs or
other processors which have been designed and verified to adhere to
computational dynamics that systematically preclude or suppress deviations. The
design and verification preclude or suppress, in particular, potential
consciousness-related dynamical effects, so that if consciousness is
dynamically relevant, AI systems cannot be conscious.
| [
{
"version": "v1",
"created": "Tue, 11 Apr 2023 09:21:17 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Nov 2023 23:00:55 GMT"
}
] | 1,699,833,600,000 | [
[
"Kleiner",
"Johannes",
""
],
[
"Ludwig",
"Tim",
""
]
] |
2304.05271 | Yash Shukla | Yash Shukla, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko
Sinapov | Automaton-Guided Curriculum Generation for Reinforcement Learning Agents | To be presented at The International Conference on Automated Planning
and Scheduling (ICAPS) 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Despite advances in Reinforcement Learning, many sequential decision making
tasks remain prohibitively expensive and impractical to learn. Recently,
approaches that automatically generate reward functions from logical task
specifications have been proposed to mitigate this issue; however, they scale
poorly on long-horizon tasks (i.e., tasks where the agent needs to perform a
series of correct actions to reach the goal state, considering future
transitions while choosing an action). Employing a curriculum (a sequence of
increasingly complex tasks) further improves the learning speed of the agent by
sequencing intermediate tasks suited to the learning capacity of the agent.
However, generating curricula from the logical specification still remains an
unsolved problem. To this end, we propose AGCL, Automaton-guided Curriculum
Learning, a novel method for automatically generating curricula for the target
task in the form of Directed Acyclic Graphs (DAGs). AGCL encodes the
specification in the form of a deterministic finite automaton (DFA), and then
uses the DFA along with the Object-Oriented MDP (OOMDP) representation to
generate a curriculum as a DAG, where the vertices correspond to tasks, and
edges correspond to the direction of knowledge transfer. Experiments in
gridworld and physics-based simulated robotics domains show that the curricula
produced by AGCL achieve improved time-to-threshold performance on a complex
sequential decision-making problem relative to state-of-the-art curriculum
learning (e.g, teacher-student, self-play) and automaton-guided reinforcement
learning baselines (e.g, Q-Learning for Reward Machines). Further, we
demonstrate that AGCL performs well even in the presence of noise in the task's
OOMDP description, and also when distractor objects are present that are not
modeled in the logical specification of the tasks' objectives.
| [
{
"version": "v1",
"created": "Tue, 11 Apr 2023 15:14:31 GMT"
}
] | 1,681,257,600,000 | [
[
"Shukla",
"Yash",
""
],
[
"Kulkarni",
"Abhishek",
""
],
[
"Wright",
"Robert",
""
],
[
"Velasquez",
"Alvaro",
""
],
[
"Sinapov",
"Jivko",
""
]
] |
2304.05493 | Uzma Hasan | Uzma Hasan, Md Osman Gani | KGS: Causal Discovery Using Knowledge-guided Greedy Equivalence Search | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Learning causal relationships solely from observational data provides
insufficient information about the underlying causal mechanism and the search
space of possible causal graphs. As a result, often the search space can grow
exponentially for approaches such as Greedy Equivalence Search (GES) that uses
a score-based approach to search the space of equivalence classes of graphs.
Prior causal information such as the presence or absence of a causal edge can
be leveraged to guide the discovery process towards a more restricted and
accurate search space. In this study, we present KGS, a knowledge-guided greedy
score-based causal discovery approach that uses observational data and
structural priors (causal edges) as constraints to learn the causal graph. KGS
is a novel application of knowledge constraints that can leverage any of the
following prior edge information between any two variables: the presence of a
directed edge, the absence of an edge, and the presence of an undirected edge.
We extensively evaluate KGS across multiple settings in both synthetic and
benchmark real-world datasets. Our experimental results demonstrate that
structural priors of any type and amount are helpful and guide the search
process towards an improved performance and early convergence.
| [
{
"version": "v1",
"created": "Tue, 11 Apr 2023 20:56:33 GMT"
}
] | 1,681,344,000,000 | [
[
"Hasan",
"Uzma",
""
],
[
"Gani",
"Md Osman",
""
]
] |
2304.06528 | Victoria Krakovna | Victoria Krakovna and Janos Kramar | Power-seeking can be probable and predictive for trained agents | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Power-seeking behavior is a key source of risk from advanced AI, but our
theoretical understanding of this phenomenon is relatively limited. Building on
existing theoretical results demonstrating power-seeking incentives for most
reward functions, we investigate how the training process affects power-seeking
incentives and show that they are still likely to hold for trained agents under
some simplifying assumptions. We formally define the training-compatible goal
set (the set of goals consistent with the training rewards) and assume that the
trained agent learns a goal from this set. In a setting where the trained agent
faces a choice to shut down or avoid shutdown in a new situation, we prove that
the agent is likely to avoid shutdown. Thus, we show that power-seeking
incentives can be probable (likely to arise for trained agents) and predictive
(allowing us to predict undesirable behavior in new situations).
| [
{
"version": "v1",
"created": "Thu, 13 Apr 2023 13:29:01 GMT"
}
] | 1,681,430,400,000 | [
[
"Krakovna",
"Victoria",
""
],
[
"Kramar",
"Janos",
""
]
] |
2304.07030 | Huizhong Guo | Huizhong Guo, Jinfeng Li, Jingyi Wang, Xiangyu Liu, Dongxia Wang,
Zehong Hu, Rong Zhang and Hui Xue | FairRec: Fairness Testing for Deep Recommender Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning-based recommender systems (DRSs) are increasingly and widely
deployed in the industry, which brings significant convenience to people's
daily life in different ways. However, recommender systems are also shown to
suffer from multiple issues,e.g., the echo chamber and the Matthew effect, of
which the notation of "fairness" plays a core role.While many fairness
notations and corresponding fairness testing approaches have been developed for
traditional deep classification models, they are essentially hardly applicable
to DRSs. One major difficulty is that there still lacks a systematic
understanding and mapping between the existing fairness notations and the
diverse testing requirements for deep recommender systems, not to mention
further testing or debugging activities. To address the gap, we propose
FairRec, a unified framework that supports fairness testing of DRSs from
multiple customized perspectives, e.g., model utility, item diversity, item
popularity, etc. We also propose a novel, efficient search-based testing
approach to tackle the new challenge, i.e., double-ended discrete particle
swarm optimization (DPSO) algorithm, to effectively search for hidden fairness
issues in the form of certain disadvantaged groups from a vast number of
candidate groups. Given the testing report, by adopting a simple re-ranking
mitigation strategy on these identified disadvantaged groups, we show that the
fairness of DRSs can be significantly improved. We conducted extensive
experiments on multiple industry-level DRSs adopted by leading companies. The
results confirm that FairRec is effective and efficient in identifying the
deeply hidden fairness issues, e.g., achieving 95% testing accuracy with half
to 1/8 time.
| [
{
"version": "v1",
"created": "Fri, 14 Apr 2023 09:49:55 GMT"
}
] | 1,681,689,600,000 | [
[
"Guo",
"Huizhong",
""
],
[
"Li",
"Jinfeng",
""
],
[
"Wang",
"Jingyi",
""
],
[
"Liu",
"Xiangyu",
""
],
[
"Wang",
"Dongxia",
""
],
[
"Hu",
"Zehong",
""
],
[
"Zhang",
"Rong",
""
],
[
"Xue",
"Hui",
""
]
] |
2304.07337 | David Radke | David Radke and Kyle Tilbury | Learning to Learn Group Alignment: A Self-Tuning Credo Framework with
Multiagent Teams | 8 pages, 6 figures, Proceedings of the Adaptive and Learning Agents
Workshop (ALA) at AAMAS 2023 | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Mixed incentives among a population with multiagent teams has been shown to
have advantages over a fully cooperative system; however, discovering the best
mixture of incentives or team structure is a difficult and dynamic problem. We
propose a framework where individual learning agents self-regulate their
configuration of incentives through various parts of their reward function.
This work extends previous work by giving agents the ability to dynamically
update their group alignment during learning and by allowing teammates to have
different group alignment. Our model builds on ideas from hierarchical
reinforcement learning and meta-learning to learn the configuration of a reward
function that supports the development of a behavioral policy. We provide
preliminary results in a commonly studied multiagent environment and find that
agents can achieve better global outcomes by self-tuning their respective group
alignment parameters.
| [
{
"version": "v1",
"created": "Fri, 14 Apr 2023 18:16:19 GMT"
}
] | 1,681,776,000,000 | [
[
"Radke",
"David",
""
],
[
"Tilbury",
"Kyle",
""
]
] |
2304.07889 | Tiago Vaz | Tiago Andres Vaz, Jos\'e Miguel Silva Dora, Lu\'is da Cunha Lamb and
Suzi Alves Camey | Ontology for Healthcare Artificial Intelligence Privacy in Brazil | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This article details the creation of a novel domain ontology at the
intersection of epidemiology, medicine, statistics, and computer science. Using
the terminology defined by current legislation, the article outlines a
systematic approach to handling hospital data anonymously in preparation for
its use in Artificial Intelligence (AI) applications in healthcare. The
development process consisted of 7 pragmatic steps, including defining scope,
selecting knowledge, reviewing important terms, constructing classes that
describe designs used in epidemiological studies, machine learning paradigms,
types of data and attributes, risks that anonymized data may be exposed to,
privacy attacks, techniques to mitigate re-identification, privacy models, and
metrics for measuring the effects of anonymization. The article concludes by
demonstrating the practical implementation of this ontology in hospital
settings for the development and validation of AI.
| [
{
"version": "v1",
"created": "Sun, 16 Apr 2023 21:05:46 GMT"
},
{
"version": "v2",
"created": "Thu, 6 Jun 2024 10:49:29 GMT"
}
] | 1,717,718,400,000 | [
[
"Vaz",
"Tiago Andres",
""
],
[
"Dora",
"José Miguel Silva",
""
],
[
"Lamb",
"Luís da Cunha",
""
],
[
"Camey",
"Suzi Alves",
""
]
] |
2304.07910 | Daqian Shi | Daqian Shi, Fausto Giunchiglia | Recognizing Entity Types via Properties | FOIS 2023 conference paper | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The mainstream approach to the development of ontologies is merging
ontologies encoding different information, where one of the major difficulties
is that the heterogeneity motivates the ontology merging but also limits
high-quality merging performance. Thus, the entity type (etype) recognition
task is proposed to deal with such heterogeneity, aiming to infer the class of
entities and etypes by exploiting the information encoded in ontologies. In
this paper, we introduce a property-based approach that allows recognizing
etypes on the basis of the properties used to define them. From an
epistemological point of view, it is in fact properties that characterize
entities and etypes, and this definition is independent of the specific labels
and hierarchical schemas used to define them. The main contribution consists of
a set of property-based metrics for measuring the contextual similarity between
etypes and entities, and a machine learning-based etype recognition algorithm
exploiting the proposed similarity metrics. Compared with the state-of-the-art,
the experimental results show the validity of the similarity metrics and the
superiority of the proposed etype recognition algorithm.
| [
{
"version": "v1",
"created": "Sun, 16 Apr 2023 22:42:30 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Apr 2023 23:59:43 GMT"
}
] | 1,682,467,200,000 | [
[
"Shi",
"Daqian",
""
],
[
"Giunchiglia",
"Fausto",
""
]
] |
2304.08293 | Florence Smith Nicholls | Florence Smith Nicholls and Michael Cook | 'That Darned Sandstorm': A Study of Procedural Generation through
Archaeological Storytelling | Published at the PCG Workshop at FDG 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Procedural content generation has been applied to many domains, especially
level design, but the narrative affordances of generated game environments are
comparatively understudied. In this paper we present our first attempt to study
these effects through the lens of what we call a generative archaeology game
that prompts the player to archaeologically interpret the generated content of
the game world. We report on a survey that gathered qualitative and
quantitative data on the experiences of 187 participants playing the game
Nothing Beside Remains. We provide some preliminary analysis of our intentional
attempt to prompt player interpretation, and the unintentional effects of a
glitch on the player experience of the game.
| [
{
"version": "v1",
"created": "Mon, 17 Apr 2023 14:08:05 GMT"
}
] | 1,681,776,000,000 | [
[
"Nicholls",
"Florence Smith",
""
],
[
"Cook",
"Michael",
""
]
] |
2304.08738 | Zhiyuan Yan | Zhiyuan Yan, Min Li, Zhengyuan Shi, Wenjie Zhang, Yingcong Chen and
Hongce Zhang | Addressing Variable Dependency in GNN-based SAT Solving | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Boolean satisfiability problem (SAT) is fundamental to many applications.
Existing works have used graph neural networks (GNNs) for (approximate) SAT
solving. Typical GNN-based end-to-end SAT solvers predict SAT solutions
concurrently. We show that for a group of symmetric SAT problems, the
concurrent prediction is guaranteed to produce a wrong answer because it
neglects the dependency among Boolean variables in SAT problems. % We propose
AsymSAT, a GNN-based architecture which integrates recurrent neural networks to
generate dependent predictions for variable assignments. The experiment results
show that dependent variable prediction extends the solving capability of the
GNN-based method as it improves the number of solved SAT instances on large
test sets.
| [
{
"version": "v1",
"created": "Tue, 18 Apr 2023 05:33:33 GMT"
}
] | 1,681,862,400,000 | [
[
"Yan",
"Zhiyuan",
""
],
[
"Li",
"Min",
""
],
[
"Shi",
"Zhengyuan",
""
],
[
"Zhang",
"Wenjie",
""
],
[
"Chen",
"Yingcong",
""
],
[
"Zhang",
"Hongce",
""
]
] |
2304.09015 | Jianhao Chen | Jianhao Chen, Junyang Ren, Wentao Ding, Yuzhong Qu | PaTeCon: A Pattern-Based Temporal Constraint Mining Method for Conflict
Detection on Knowledge Graphs | Accepted by AAAI23 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal facts, the facts for characterizing events that hold in specific
time periods, are attracting rising attention in the knowledge graph (KG)
research communities. In terms of quality management, the introduction of time
restrictions brings new challenges to maintaining the temporal consistency of
KGs and detecting potential temporal conflicts. Previous studies rely on
manually enumerated temporal constraints to detect conflicts, which are
labor-intensive and may have granularity issues. We start from the common
pattern of temporal facts and constraints and propose a pattern-based temporal
constraint mining method, PaTeCon. PaTeCon uses automatically determined graph
patterns and their relevant statistical information over the given KG instead
of human experts to generate time constraints. Specifically, PaTeCon
dynamically attaches class restriction to candidate constraints according to
their measuring scores.We evaluate PaTeCon on two large-scale datasets based on
Wikidata and Freebase respectively. The experimental results show that
pattern-based automatic constraint mining is powerful in generating valuable
temporal constraints.
| [
{
"version": "v1",
"created": "Tue, 18 Apr 2023 14:28:35 GMT"
},
{
"version": "v2",
"created": "Sun, 23 Apr 2023 13:00:26 GMT"
},
{
"version": "v3",
"created": "Fri, 12 May 2023 14:48:00 GMT"
}
] | 1,684,108,800,000 | [
[
"Chen",
"Jianhao",
""
],
[
"Ren",
"Junyang",
""
],
[
"Ding",
"Wentao",
""
],
[
"Qu",
"Yuzhong",
""
]
] |
2304.09395 | Yan Jin | Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song,
Jiang Bian | H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman
Problem | Accepted by AAAI 2023, February 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an end-to-end learning framework based on hierarchical
reinforcement learning, called H-TSP, for addressing the large-scale Travelling
Salesman Problem (TSP). The proposed H-TSP constructs a solution of a TSP
instance starting from the scratch relying on two components: the upper-level
policy chooses a small subset of nodes (up to 200 in our experiment) from all
nodes that are to be traversed, while the lower-level policy takes the chosen
nodes as input and outputs a tour connecting them to the existing partial route
(initially only containing the depot). After jointly training the upper-level
and lower-level policies, our approach can directly generate solutions for the
given TSP instances without relying on any time-consuming search procedures. To
demonstrate effectiveness of the proposed approach, we have conducted extensive
experiments on randomly generated TSP instances with different numbers of
nodes. We show that H-TSP can achieve comparable results (gap 3.42% vs. 7.32%)
as SOTA search-based approaches, and more importantly, we reduce the time
consumption up to two orders of magnitude (3.32s vs. 395.85s). To the best of
our knowledge, H-TSP is the first end-to-end deep reinforcement learning
approach that can scale to TSP instances of up to 10000 nodes. Although there
are still gaps to SOTA results with respect to solution quality, we believe
that H-TSP will be useful for practical applications, particularly those that
are time-sensitive e.g., on-call routing and ride hailing service.
| [
{
"version": "v1",
"created": "Wed, 19 Apr 2023 03:10:30 GMT"
}
] | 1,681,948,800,000 | [
[
"Pan",
"Xuanhao",
""
],
[
"Jin",
"Yan",
""
],
[
"Ding",
"Yuandong",
""
],
[
"Feng",
"Mingxiao",
""
],
[
"Zhao",
"Li",
""
],
[
"Song",
"Lei",
""
],
[
"Bian",
"Jiang",
""
]
] |
2304.09407 | Yan Jin | Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He, Li Zhao, Tao Qin, Lei
Song, Jiang Bian | Pointerformer: Deep Reinforced Multi-Pointer Transformer for the
Traveling Salesman Problem | Accepted by AAAI 2023, February 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traveling Salesman Problem (TSP), as a classic routing optimization problem
originally arising in the domain of transportation and logistics, has become a
critical task in broader domains, such as manufacturing and biology. Recently,
Deep Reinforcement Learning (DRL) has been increasingly employed to solve TSP
due to its high inference efficiency. Nevertheless, most of existing end-to-end
DRL algorithms only perform well on small TSP instances and can hardly
generalize to large scale because of the drastically soaring memory consumption
and computation time along with the enlarging problem scale. In this paper, we
propose a novel end-to-end DRL approach, referred to as Pointerformer, based on
multi-pointer Transformer. Particularly, Pointerformer adopts both reversible
residual network in the encoder and multi-pointer network in the decoder to
effectively contain memory consumption of the encoder-decoder architecture. To
further improve the performance of TSP solutions, Pointerformer employs both a
feature augmentation method to explore the symmetries of TSP at both training
and inference stages as well as an enhanced context embedding approach to
include more comprehensive context information in the query. Extensive
experiments on a randomly generated benchmark and a public benchmark have shown
that, while achieving comparative results on most small-scale TSP instances as
SOTA DRL approaches do, Pointerformer can also well generalize to large-scale
TSPs.
| [
{
"version": "v1",
"created": "Wed, 19 Apr 2023 03:48:32 GMT"
}
] | 1,681,948,800,000 | [
[
"Jin",
"Yan",
""
],
[
"Ding",
"Yuandong",
""
],
[
"Pan",
"Xuanhao",
""
],
[
"He",
"Kun",
""
],
[
"Zhao",
"Li",
""
],
[
"Qin",
"Tao",
""
],
[
"Song",
"Lei",
""
],
[
"Bian",
"Jiang",
""
]
] |
2304.09769 | Soichiro Nishimori | Soichiro Nishimori, Sotetsu Koyamada and Shin Ishii | End-to-End Policy Gradient Method for POMDPs and Explainable Agents | 10 pagee, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-world decision-making problems are often partially observable, and many
can be formulated as a Partially Observable Markov Decision Process (POMDP).
When we apply reinforcement learning (RL) algorithms to the POMDP, reasonable
estimation of the hidden states can help solve the problems. Furthermore,
explainable decision-making is preferable, considering their application to
real-world tasks such as autonomous driving cars. We proposed an RL algorithm
that estimates the hidden states by end-to-end training, and visualize the
estimation as a state-transition graph. Experimental results demonstrated that
the proposed algorithm can solve simple POMDP problems and that the
visualization makes the agent's behavior interpretable to humans.
| [
{
"version": "v1",
"created": "Wed, 19 Apr 2023 15:45:52 GMT"
}
] | 1,681,948,800,000 | [
[
"Nishimori",
"Soichiro",
""
],
[
"Koyamada",
"Sotetsu",
""
],
[
"Ishii",
"Shin",
""
]
] |
2304.09970 | Jeroen Middelhuis | J. Middelhuis, R. Lo Bianco, E. Scherzer, Z. A. Bukhsh, I. J. B. F.
Adan, R. M. Dijkman | Learning policies for resource allocation in business processes | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Efficient allocation of resources to activities is pivotal in executing
business processes but remains challenging. While resource allocation
methodologies are well-established in domains like manufacturing, their
application within business process management remains limited. Existing
methods often do not scale well to large processes with numerous activities or
optimize across multiple cases. This paper aims to address this gap by
proposing two learning-based methods for resource allocation in business
processes. The first method leverages Deep Reinforcement Learning (DRL) to
learn near-optimal policies by taking action in the business process. The
second method is a score-based value function approximation approach, which
learns the weights of a set of curated features to prioritize resource
assignments. To evaluate the proposed approaches, we first designed six
distinct business processes with archetypal process flows and characteristics.
These business processes were then connected to form three realistically sized
business processes. We benchmarked our methods against traditional heuristics
and existing resource allocation methods. The results show that our methods
learn adaptive resource allocation policies that outperform or are competitive
with the benchmarks in five out of six individual business processes. The DRL
approach outperforms all benchmarks in all three composite business processes
and finds a policy that is, on average, 13.1% better than the best-performing
benchmark.
| [
{
"version": "v1",
"created": "Wed, 19 Apr 2023 21:05:38 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Jan 2024 11:36:51 GMT"
}
] | 1,706,054,400,000 | [
[
"Middelhuis",
"J.",
""
],
[
"Bianco",
"R. Lo",
""
],
[
"Scherzer",
"E.",
""
],
[
"Bukhsh",
"Z. A.",
""
],
[
"Adan",
"I. J. B. F.",
""
],
[
"Dijkman",
"R. M.",
""
]
] |
2304.10427 | Ana Claudia Sima | Ana-Claudia Sima and Tarcisio Mendes de Farias | On the Potential of Artificial Intelligence Chatbots for Data
Exploration of Federated Bioinformatics Knowledge Graphs | null | null | null | https://ceur-ws.org/Vol-3466/paper1.pdf | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we present work in progress on the role of artificial
intelligence (AI) chatbots, such as ChatGPT, in facilitating data access to
federated knowledge graphs. In particular, we provide examples from the field
of bioinformatics, to illustrate the potential use of Conversational AI to
describe datasets, as well as generate and explain (federated) queries across
datasets for the benefit of domain experts.
| [
{
"version": "v1",
"created": "Thu, 20 Apr 2023 16:16:40 GMT"
}
] | 1,695,772,800,000 | [
[
"Sima",
"Ana-Claudia",
""
],
[
"de Farias",
"Tarcisio Mendes",
""
]
] |
2304.10590 | Carlos N\'u\~nez Molina | Carlos N\'u\~nez-Molina, Pablo Mesejo, Juan Fern\'andez-Olivares | A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential
Decision Making | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The field of Sequential Decision Making (SDM) provides tools for solving
Sequential Decision Processes (SDPs), where an agent must make a series of
decisions in order to complete a task or achieve a goal. Historically, two
competing SDM paradigms have view for supremacy. Automated Planning (AP)
proposes to solve SDPs by performing a reasoning process over a model of the
world, often represented symbolically. Conversely, Reinforcement Learning (RL)
proposes to learn the solution of the SDP from data, without a world model, and
represent the learned knowledge subsymbolically. In the spirit of
reconciliation, we provide a review of symbolic, subsymbolic and hybrid methods
for SDM. We cover both methods for solving SDPs (e.g., AP, RL and techniques
that learn to plan) and for learning aspects of their structure (e.g., world
models, state invariants and landmarks). To the best of our knowledge, no other
review in the field provides the same scope. As an additional contribution, we
discuss what properties an ideal method for SDM should exhibit and argue that
neurosymbolic AI is the current approach which most closely resembles this
ideal method. Finally, we outline several proposals to advance the field of SDM
via the integration of symbolic and subsymbolic AI.
| [
{
"version": "v1",
"created": "Thu, 20 Apr 2023 18:22:30 GMT"
}
] | 1,682,294,400,000 | [
[
"Núñez-Molina",
"Carlos",
""
],
[
"Mesejo",
"Pablo",
""
],
[
"Fernández-Olivares",
"Juan",
""
]
] |
2304.10596 | Akhil K | Akhil Kuniyil, Avinash Kshitij, and Kasturi Mandal | Enhancing Artificial intelligence Policies with Fusion and Forecasting:
Insights from Indian Patents Using Network Analysis | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper presents a study of the interconnectivity and interdependence of
various Artificial intelligence (AI) technologies through the use of centrality
measures, clustering coefficients, and degree of fusion measures. By analyzing
the technologies through different time windows and quantifying their
importance, we have revealed important insights into the crucial components
shaping the AI landscape and the maturity level of the domain. The results of
this study have significant implications for future development and
advancements in artificial intelligence and provide a clear understanding of
key technology areas of fusion. Furthermore, this paper contributes to AI
public policy research by offering a data-driven perspective on the current
state and future direction of the field. However, it is important to
acknowledge the limitations of this research and call for further studies to
build on these results. With these findings, we hope to inform and guide future
research in the field of AI, contributing to its continued growth and success.
| [
{
"version": "v1",
"created": "Thu, 20 Apr 2023 18:37:11 GMT"
}
] | 1,682,294,400,000 | [
[
"Kuniyil",
"Akhil",
""
],
[
"Kshitij",
"Avinash",
""
],
[
"Mandal",
"Kasturi",
""
]
] |
2304.11104 | Alexander W. Goodall | Alexander W. Goodall and Francesco Belardinelli | Approximate Shielding of Atari Agents for Safe Exploration | Accepted for presentation at the ALA workshop as part of AAMAS 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Balancing exploration and conservatism in the constrained setting is an
important problem if we are to use reinforcement learning for meaningful tasks
in the real world. In this paper, we propose a principled algorithm for safe
exploration based on the concept of shielding. Previous approaches to shielding
assume access to a safety-relevant abstraction of the environment or a
high-fidelity simulator. Instead, our work is based on latent shielding -
another approach that leverages world models to verify policy roll-outs in the
latent space of a learned dynamics model. Our novel algorithm builds on this
previous work, using safety critics and other additional features to improve
the stability and farsightedness of the algorithm. We demonstrate the
effectiveness of our approach by running experiments on a small set of Atari
games with state dependent safety labels. We present preliminary results that
show our approximate shielding algorithm effectively reduces the rate of safety
violations, and in some cases improves the speed of convergence and quality of
the final agent.
| [
{
"version": "v1",
"created": "Fri, 21 Apr 2023 16:19:54 GMT"
}
] | 1,682,294,400,000 | [
[
"Goodall",
"Alexander W.",
""
],
[
"Belardinelli",
"Francesco",
""
]
] |
2304.11124 | Giancarlo Guizzardi | Giancarlo Guizzardi, Nicola Guarino | Semantics, Ontology and Explanation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The terms 'semantics' and 'ontology' are increasingly appearing together with
'explanation', not only in the scientific literature, but also in
organizational communication. However, all of these terms are also being
significantly overloaded. In this paper, we discuss their strong relation under
particular interpretations. Specifically, we discuss a notion of explanation
termed ontological unpacking, which aims at explaining symbolic domain
descriptions (conceptual models, knowledge graphs, logical specifications) by
revealing their ontological commitment in terms of their assumed truthmakers,
i.e., the entities in one's ontology that make the propositions in those
descriptions true. To illustrate this idea, we employ an ontological theory of
relations to explain (by revealing the hidden semantics of) a very simple
symbolic model encoded in the standard modeling language UML. We also discuss
the essential role played by ontology-driven conceptual models (resulting from
this form of explanation processes) in properly supporting semantic
interoperability tasks. Finally, we discuss the relation between ontological
unpacking and other forms of explanation in philosophy and science, as well as
in the area of Artificial Intelligence.
| [
{
"version": "v1",
"created": "Fri, 21 Apr 2023 16:54:34 GMT"
}
] | 1,682,294,400,000 | [
[
"Guizzardi",
"Giancarlo",
""
],
[
"Guarino",
"Nicola",
""
]
] |
2304.11318 | Yueyang Liu | Yueyang Liu, Zois Boukouvalas, and Nathalie Japkowicz | A Semi-Supervised Framework for Misinformation Detection | null | In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021.
Lecture Notes in Computer Science(), vol 12986. Springer, Cham | 10.1007/978-3-030-88942-5_5 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The spread of misinformation in social media outlets has become a prevalent
societal problem and is the cause of many kinds of social unrest. Curtailing
its prevalence is of great importance and machine learning has shown
significant promise. However, there are two main challenges when applying
machine learning to this problem. First, while much too prevalent in one
respect, misinformation, actually, represents only a minor proportion of all
the postings seen on social media. Second, labeling the massive amount of data
necessary to train a useful classifier becomes impractical. Considering these
challenges, we propose a simple semi-supervised learning framework in order to
deal with extreme class imbalances that has the advantage, over other
approaches, of using actual rather than simulated data to inflate the minority
class. We tested our framework on two sets of Covid-related Twitter data and
obtained significant improvement in F1-measure on extremely imbalanced
scenarios, as compared to simple classical and deep-learning data generation
methods such as SMOTE, ADASYN, or GAN-based data generation.
| [
{
"version": "v1",
"created": "Sat, 22 Apr 2023 05:20:58 GMT"
}
] | 1,682,380,800,000 | [
[
"Liu",
"Yueyang",
""
],
[
"Boukouvalas",
"Zois",
""
],
[
"Japkowicz",
"Nathalie",
""
]
] |
2304.11376 | Ken Hasselmann | Ken Hasselmann, Quentin Lurkin | Stimulating student engagement with an AI board game tournament | Presented in Teaching Machine Learning Workshop at ECML 2022
(https://teaching-ml.github.io/2022/) | Proceedings of Machine Learning Research, 2023 | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Strong foundations in basic AI techniques are key to understanding more
advanced concepts. We believe that introducing AI techniques, such as search
methods, early in higher education helps create a deeper understanding of the
concepts seen later in more advanced AI and algorithms courses. We present a
project-based and competition-based bachelor course that gives second-year
students an introduction to search methods applied to board games. In groups of
two, students have to use network programming and AI methods to build an AI
agent to compete in a board game tournament-othello was this year's game.
Students are evaluated based on the quality of their projects and on their
performance during the final tournament. We believe that the introduction of
gamification, in the form of competition-based learning, allows for a better
learning experience for the students.
| [
{
"version": "v1",
"created": "Sat, 22 Apr 2023 11:22:00 GMT"
}
] | 1,714,089,600,000 | [
[
"Hasselmann",
"Ken",
""
],
[
"Lurkin",
"Quentin",
""
]
] |
2304.11383 | Huanhuan Yuan | Huanhuan Yuan, Pengpeng Zhao, Xuefeng Xian and Guanfeng Liu and Victor
S. Sheng and Lei Zhao | Sequential Recommendation with Probabilistic Logical Reasoning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning and symbolic learning are two frequently employed methods in
Sequential Recommendation (SR). Recent neural-symbolic SR models demonstrate
their potential to enable SR to be equipped with concurrent perception and
cognition capacities. However, neural-symbolic SR remains a challenging problem
due to open issues like representing users and items in logical reasoning. In
this paper, we combine the Deep Neural Network (DNN) SR models with logical
reasoning and propose a general framework named Sequential Recommendation with
Probabilistic Logical Reasoning (short for SR-PLR). This framework allows
SR-PLR to benefit from both similarity matching and logical reasoning by
disentangling feature embedding and logic embedding in the DNN and
probabilistic logic network. To better capture the uncertainty and evolution of
user tastes, SR-PLR embeds users and items with a probabilistic method and
conducts probabilistic logical reasoning on users' interaction patterns. Then
the feature and logic representations learned from the DNN and logic network
are concatenated to make the prediction. Finally, experiments on various
sequential recommendation models demonstrate the effectiveness of the SR-PLR.
| [
{
"version": "v1",
"created": "Sat, 22 Apr 2023 12:25:40 GMT"
},
{
"version": "v2",
"created": "Mon, 15 May 2023 14:39:49 GMT"
}
] | 1,684,195,200,000 | [
[
"Yuan",
"Huanhuan",
""
],
[
"Zhao",
"Pengpeng",
""
],
[
"Xian",
"Xuefeng",
""
],
[
"Liu",
"Guanfeng",
""
],
[
"Sheng",
"Victor S.",
""
],
[
"Zhao",
"Lei",
""
]
] |
2304.11411 | Heng Wang | Heng Wang, Wenqian Zhang, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng,
Qinghua Zheng, Minnan Luo | Detecting Spoilers in Movie Reviews with External Movie Knowledge and
User Networks | EMNLP 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Online movie review platforms are providing crowdsourced feedback for the
film industry and the general public, while spoiler reviews greatly compromise
user experience. Although preliminary research efforts were made to
automatically identify spoilers, they merely focus on the review content
itself, while robust spoiler detection requires putting the review into the
context of facts and knowledge regarding movies, user behavior on film review
platforms, and more. In light of these challenges, we first curate a
large-scale network-based spoiler detection dataset LCS and a comprehensive and
up-to-date movie knowledge base UKM. We then propose MVSD, a novel Multi-View
Spoiler Detection framework that takes into account the external knowledge
about movies and user activities on movie review platforms. Specifically, MVSD
constructs three interconnecting heterogeneous information networks to model
diverse data sources and their multi-view attributes, while we design and
employ a novel heterogeneous graph neural network architecture for spoiler
detection as node-level classification. Extensive experiments demonstrate that
MVSD advances the state-of-the-art on two spoiler detection datasets, while the
introduction of external knowledge and user interactions help ground robust
spoiler detection. Our data and code are available at
https://github.com/Arthur-Heng/Spoiler-Detection
| [
{
"version": "v1",
"created": "Sat, 22 Apr 2023 13:54:31 GMT"
},
{
"version": "v2",
"created": "Thu, 26 Oct 2023 04:07:19 GMT"
}
] | 1,698,364,800,000 | [
[
"Wang",
"Heng",
""
],
[
"Zhang",
"Wenqian",
""
],
[
"Bai",
"Yuyang",
""
],
[
"Tan",
"Zhaoxuan",
""
],
[
"Feng",
"Shangbin",
""
],
[
"Zheng",
"Qinghua",
""
],
[
"Luo",
"Minnan",
""
]
] |
2304.11513 | Yue Hu | Yue Hu, Yuhang Zhang, Yanbing Wang, Daniel Work | Detecting Socially Abnormal Highway Driving Behaviors via Recurrent
Graph Attention Networks | null | null | 10.1145/3543507.3583452. | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With the rapid development of Internet of Things technologies, the next
generation traffic monitoring infrastructures are connected via the web, to aid
traffic data collection and intelligent traffic management. One of the most
important tasks in traffic is anomaly detection, since abnormal drivers can
reduce traffic efficiency and cause safety issues. This work focuses on
detecting abnormal driving behaviors from trajectories produced by highway
video surveillance systems. Most of the current abnormal driving behavior
detection methods focus on a limited category of abnormal behaviors that deal
with a single vehicle without considering vehicular interactions. In this work,
we consider the problem of detecting a variety of socially abnormal driving
behaviors, i.e., behaviors that do not conform to the behavior of other nearby
drivers. This task is complicated by the variety of vehicular interactions and
the spatial-temporal varying nature of highway traffic. To solve this problem,
we propose an autoencoder with a Recurrent Graph Attention Network that can
capture the highway driving behaviors contextualized on the surrounding cars,
and detect anomalies that deviate from learned patterns. Our model is scalable
to large freeways with thousands of cars. Experiments on data generated from
traffic simulation software show that our model is the only one that can spot
the exact vehicle conducting socially abnormal behaviors, among the
state-of-the-art anomaly detection models. We further show the performance on
real world HighD traffic dataset, where our model detects vehicles that violate
the local driving norms.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2023 01:32:47 GMT"
}
] | 1,682,380,800,000 | [
[
"Hu",
"Yue",
""
],
[
"Zhang",
"Yuhang",
""
],
[
"Wang",
"Yanbing",
""
],
[
"Work",
"Daniel",
""
]
] |
2304.11524 | Jianzong Wang | Rongfeng Pan, Jianzong Wang, Lingwei Kong, Zhangcheng Huang, Jing Xiao | Personalized Federated Learning via Gradient Modulation for
Heterogeneous Text Summarization | Accepted by IJCNN2023. 2023 IEEE International Joint Conference on
Neural Network (IJCNN2023) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Text summarization is essential for information aggregation and demands large
amounts of training data. However, concerns about data privacy and security
limit data collection and model training. To eliminate this concern, we propose
a federated learning text summarization scheme, which allows users to share the
global model in a cooperative learning manner without sharing raw data.
Personalized federated learning (PFL) balances personalization and
generalization in the process of optimizing the global model, to guide the
training of local models. However, multiple local data have different
distributions of semantics and context, which may cause the local model to
learn deviated semantic and context information. In this paper, we propose
FedSUMM, a dynamic gradient adapter to provide more appropriate local
parameters for local model. Simultaneously, FedSUMM uses differential privacy
to prevent parameter leakage during distributed training. Experimental evidence
verifies FedSUMM can achieve faster model convergence on PFL algorithm for
task-specific text summarization, and the method achieves superior performance
for different optimization metrics for text summarization.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2023 03:18:46 GMT"
}
] | 1,682,380,800,000 | [
[
"Pan",
"Rongfeng",
""
],
[
"Wang",
"Jianzong",
""
],
[
"Kong",
"Lingwei",
""
],
[
"Huang",
"Zhangcheng",
""
],
[
"Xiao",
"Jing",
""
]
] |
2304.11530 | Debesh Jha | Debesh Jha, Ashish Rauniyar, Abhiskek Srivastava, Desta Haileselassie
Hagos, Nikhil Kumar Tomar, Vanshali Sharma, Elif Keles, Zheyuan Zhang, Ugur
Demir, Ahmet Topcu, Anis Yazidi, Jan Erik H{\aa}akeg{\aa}rd, and Ulas Bagci | Ensuring Trustworthy Medical Artificial Intelligence through Ethical and
Philosophical Principles | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial intelligence (AI) methods hold immense potential to revolutionize
numerous medical care by enhancing the experience of medical experts and
patients. AI-based computer-assisted diagnosis and treatment tools can
democratize healthcare by matching the clinical level or surpassing clinical
experts. As a result, advanced healthcare services can be affordable to all
populations, irrespective of demographics, race, or socioeconomic background.
The democratization of such AI tools can reduce the cost of care, optimize
resource allocation, and improve the quality of care. In contrast to humans, AI
can uncover complex relations in the data from a large set of inputs and even
lead to new evidence-based knowledge in medicine. However, integrating AI into
healthcare raises several ethical and philosophical concerns, such as bias,
transparency, autonomy, responsibility, and accountability. Here, we emphasize
recent advances in AI-assisted medical image analysis, existing standards, and
the significance of comprehending ethical issues and best practices for
clinical settings. We cover the technical and ethical challenges and
implications of deploying AI in hospitals and public organizations. We also
discuss key measures and techniques to address ethical challenges, data
scarcity, racial bias, lack of transparency, and algorithmic bias and provide
recommendations and future directions.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2023 04:14:18 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Apr 2023 02:51:39 GMT"
},
{
"version": "v3",
"created": "Sat, 29 Apr 2023 15:42:24 GMT"
},
{
"version": "v4",
"created": "Thu, 21 Sep 2023 00:10:48 GMT"
}
] | 1,695,340,800,000 | [
[
"Jha",
"Debesh",
""
],
[
"Rauniyar",
"Ashish",
""
],
[
"Srivastava",
"Abhiskek",
""
],
[
"Hagos",
"Desta Haileselassie",
""
],
[
"Tomar",
"Nikhil Kumar",
""
],
[
"Sharma",
"Vanshali",
""
],
[
"Keles",
"Elif",
""
],
[
"Zhang",
"Zheyuan",
""
],
[
"Demir",
"Ugur",
""
],
[
"Topcu",
"Ahmet",
""
],
[
"Yazidi",
"Anis",
""
],
[
"Håakegård",
"Jan Erik",
""
],
[
"Bagci",
"Ulas",
""
]
] |
2304.11574 | Chao Li | Chao Li, Hao Xu, Kun He | Meta-multigraph Search: Rethinking Meta-structure on Heterogeneous
Information Networks | 17 pages, 10 figures. arXiv admin note: text overlap with
arXiv:2211.14752 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Meta-structures are widely used to define which subset of neighbors to
aggregate information in heterogeneous information networks (HINs). In this
work, we investigate existing meta-structures, including meta-path and
meta-graph, and observe that they are initially designed manually with fixed
patterns and hence are insufficient to encode various rich semantic information
on diverse HINs. Through reflection on their limitation, we define a new
concept called meta-multigraph as a more expressive and flexible generalization
of meta-graph, and propose a stable differentiable search method to
automatically optimize the meta-multigraph for specific HINs and tasks. As the
flexibility of meta-multigraphs may propagate redundant messages, we further
introduce a complex-to-concise (C2C) meta-multigraph that propagates messages
from complex to concise along the depth of meta-multigraph. Moreover, we
observe that the differentiable search typically suffers from unstable search
and a significant gap between the meta-structures in search and evaluation. To
this end, we propose a progressive search algorithm by implicitly narrowing the
search space to improve search stability and reduce inconsistency. Extensive
experiments are conducted on six medium-scale benchmark datasets and one
large-scale benchmark dataset over two representative tasks, i.e., node
classification and recommendation. Empirical results demonstrate that our
search methods can automatically find expressive meta-multigraphs and C2C
meta-multigraphs, enabling our model to outperform state-of-the-art
heterogeneous graph neural networks.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2023 08:15:20 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jul 2023 14:57:28 GMT"
}
] | 1,689,206,400,000 | [
[
"Li",
"Chao",
""
],
[
"Xu",
"Hao",
""
],
[
"He",
"Kun",
""
]
] |
2304.11632 | Yiming Gao | Yiming Gao, Feiyu Liu, Liang Wang, Zhenjie Lian, Weixuan Wang, Siqin
Li, Xianliang Wang, Xianhan Zeng, Rundong Wang, Jiawei Wang, Qiang Fu, Wei
Yang, Lanxiao Huang, Wei Liu | Towards Effective and Interpretable Human-Agent Collaboration in MOBA
Games: A Communication Perspective | Accepted at ICLR 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | MOBA games, e.g., Dota2 and Honor of Kings, have been actively used as the
testbed for the recent AI research on games, and various AI systems have been
developed at the human level so far. However, these AI systems mainly focus on
how to compete with humans, less on exploring how to collaborate with humans.
To this end, this paper makes the first attempt to investigate human-agent
collaboration in MOBA games. In this paper, we propose to enable humans and
agents to collaborate through explicit communication by designing an efficient
and interpretable Meta-Command Communication-based framework, dubbed MCC, for
accomplishing effective human-agent collaboration in MOBA games. The MCC
framework consists of two pivotal modules: 1) an interpretable communication
protocol, i.e., the Meta-Command, to bridge the communication gap between
humans and agents; 2) a meta-command value estimator, i.e., the Meta-Command
Selector, to select a valuable meta-command for each agent to achieve effective
human-agent collaboration. Experimental results in Honor of Kings demonstrate
that MCC agents can collaborate reasonably well with human teammates and even
generalize to collaborate with different levels and numbers of human teammates.
Videos are available at https://sites.google.com/view/mcc-demo.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2023 12:11:04 GMT"
}
] | 1,682,380,800,000 | [
[
"Gao",
"Yiming",
""
],
[
"Liu",
"Feiyu",
""
],
[
"Wang",
"Liang",
""
],
[
"Lian",
"Zhenjie",
""
],
[
"Wang",
"Weixuan",
""
],
[
"Li",
"Siqin",
""
],
[
"Wang",
"Xianliang",
""
],
[
"Zeng",
"Xianhan",
""
],
[
"Wang",
"Rundong",
""
],
[
"Wang",
"Jiawei",
""
],
[
"Fu",
"Qiang",
""
],
[
"Yang",
"Wei",
""
],
[
"Huang",
"Lanxiao",
""
],
[
"Liu",
"Wei",
""
]
] |
2304.11703 | Md. Tarek Hasan Mr. | Md. Tarek Hasan, Mohammad Nazmush Shamael, Arifa Akter, Rokibul Islam,
Md. Saddam Hossain Mukta, Salekul Islam | An Artificial Intelligence-based Framework to Achieve the Sustainable
Development Goals in the Context of Bangladesh | 11 pages, 5 figures, This is a part of the Proceedings of the 5th
International Conference on Sustainable Development, Published by Institute
of Development Studies and Sustainable Development (IDSS), United
International University, United City, Madani Avenue, Badda, Dhaka 1212,
Bangladesh, Link:
icsd.uiu.ac.bd/wp-content/uploads/2022/11/5th-UIU-ICSD-2022-Proceedings..pdf | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sustainable development is a framework for achieving human development goals.
It provides natural systems' ability to deliver natural resources and ecosystem
services. Sustainable development is crucial for the economy and society.
Artificial intelligence (AI) has attracted increasing attention in recent
years, with the potential to have a positive influence across many domains. AI
is a commonly employed component in the quest for long-term sustainability. In
this study, we explore the impact of AI on three pillars of sustainable
development: society, environment, and economy, as well as numerous case
studies from which we may deduce the impact of AI in a variety of areas, i.e.,
agriculture, classifying waste, smart water management, and Heating,
Ventilation, and Air Conditioning (HVAC) systems. Furthermore, we present
AI-based strategies for achieving Sustainable Development Goals (SDGs) which
are effective for developing countries like Bangladesh. The framework that we
propose may reduce the negative impact of AI and promote the proactiveness of
this technology.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2023 17:36:37 GMT"
}
] | 1,682,380,800,000 | [
[
"Hasan",
"Md. Tarek",
""
],
[
"Shamael",
"Mohammad Nazmush",
""
],
[
"Akter",
"Arifa",
""
],
[
"Islam",
"Rokibul",
""
],
[
"Mukta",
"Md. Saddam Hossain",
""
],
[
"Islam",
"Salekul",
""
]
] |
2304.11722 | Zhenwei Tang | Zhenwei Tang, Griffin Floto, Armin Toroghi, Shichao Pei, Xiangliang
Zhang, Scott Sanner | LogicRec: Recommendation with Users' Logical Requirements | SIGIR 2023 | null | 10.1145/3539618.3592012 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Users may demand recommendations with highly personalized requirements
involving logical operations, e.g., the intersection of two requirements, where
such requirements naturally form structured logical queries on knowledge graphs
(KGs). To date, existing recommender systems lack the capability to tackle
users' complex logical requirements. In this work, we formulate the problem of
recommendation with users' logical requirements (LogicRec) and construct
benchmark datasets for LogicRec. Furthermore, we propose an initial solution
for LogicRec based on logical requirement retrieval and user preference
retrieval, where we face two challenges. First, KGs are incomplete in nature.
Therefore, there are always missing true facts, which entails that the answers
to logical requirements can not be completely found in KGs. In this case, item
selection based on the answers to logical queries is not applicable. We thus
resort to logical query embedding (LQE) to jointly infer missing facts and
retrieve items based on logical requirements. Second, answer sets are
under-exploited. Existing LQE methods can only deal with query-answer pairs,
where queries in our case are the intersected user preferences and logical
requirements. However, the logical requirements and user preferences have
different answer sets, offering us richer knowledge about the requirements and
preferences by providing requirement-item and preference-item pairs. Thus, we
design a multi-task knowledge-sharing mechanism to exploit these answer sets
collectively. Extensive experimental results demonstrate the significance of
the LogicRec task and the effectiveness of our proposed method.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2023 18:46:58 GMT"
}
] | 1,682,380,800,000 | [
[
"Tang",
"Zhenwei",
""
],
[
"Floto",
"Griffin",
""
],
[
"Toroghi",
"Armin",
""
],
[
"Pei",
"Shichao",
""
],
[
"Zhang",
"Xiangliang",
""
],
[
"Sanner",
"Scott",
""
]
] |
2304.11733 | Subhrangshu Adhikary | Subhrangshu Adhikary, Sonam Chaturvedi, Sudhir Kumar Chaturvedi and
Saikat Banerjee | COVID-19 Spreading Prediction and Impact Analysis by Using Artificial
Intelligence for Sustainable Global Health Assessment | Advances in Environment Engineering and Management. Year 2021.
Springer Proceedings in Earth and Environmental Sciences. Springer, Cham.
https://doi.org/10.1007/978-3-030-79065-3_30 | Advances in Environment Engineering and Management. Year 2021.
Springer Proceedings in Earth and Environmental Sciences. Springer, Cham | 10.1007/978-3-030-79065-3_30 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The COVID-19 pandemic is considered as the most alarming global health
calamity of this century. COVID-19 has been confirmed to be mutated from
coronavirus family. As stated by the records of The World Health Organization
(WHO at April 18 2020), the present epidemic of COVID-19, has influenced more
than 2,164,111 persons and killed more than 146,198 folks in over 200 countries
across the globe and billions had confronted impacts in lifestyle because of
this virus outbreak. The ongoing overall outbreak of the COVID-19 opened up new
difficulties to the research sectors. Artificial intelligence (AI) driven
strategies can be valuable to predict the parameters, hazards, and impacts of
such an epidemic in a cost-efficient manner. The fundamental difficulties of AI
in this situation is the limited availability of information and the uncertain
nature of the disease. Here in this article, we have tried to integrate AI to
predict the infection outbreak and along with this, we have also tried to test
whether AI with help deep learning can recognize COVID-19 infected chest X-Rays
or not. The global outbreak of the virus posed enormous economic, ecological
and societal challenges into the human population and with help of this paper,
we have tried to give a message that AI can help us to identify certain
features of the disease outbreak that could prove to be essential to protect
the humanity from this deadly disease.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2023 19:48:29 GMT"
}
] | 1,698,969,600,000 | [
[
"Adhikary",
"Subhrangshu",
""
],
[
"Chaturvedi",
"Sonam",
""
],
[
"Chaturvedi",
"Sudhir Kumar",
""
],
[
"Banerjee",
"Saikat",
""
]
] |
2304.11740 | Sariah Mghames Dr | Sariah Mghames, Luca Castri, Marc Hanheide, Nicola Bellotto | A Neuro-Symbolic Approach for Enhanced Human Motion Prediction | International Joint Conference on Neural Networks (IJCNN), 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Reasoning on the context of human beings is crucial for many real-world
applications especially for those deploying autonomous systems (e.g. robots).
In this paper, we present a new approach for context reasoning to further
advance the field of human motion prediction. We therefore propose a
neuro-symbolic approach for human motion prediction (NeuroSyM), which weights
differently the interactions in the neighbourhood by leveraging an intuitive
technique for spatial representation called Qualitative Trajectory Calculus
(QTC). The proposed approach is experimentally tested on medium and long term
time horizons using two architectures from the state of art, one of which is a
baseline for human motion prediction and the other is a baseline for generic
multivariate time-series prediction. Six datasets of challenging crowded
scenarios, collected from both fixed and mobile cameras, were used for testing.
Experimental results show that the NeuroSyM approach outperforms in most cases
the baseline architectures in terms of prediction accuracy.
| [
{
"version": "v1",
"created": "Sun, 23 Apr 2023 20:11:40 GMT"
}
] | 1,682,380,800,000 | [
[
"Mghames",
"Sariah",
""
],
[
"Castri",
"Luca",
""
],
[
"Hanheide",
"Marc",
""
],
[
"Bellotto",
"Nicola",
""
]
] |
2304.11794 | Jun Wu | Jun Wu, Xuesong Ye, Chengjie Mou and Weinan Dai | FineEHR: Refine Clinical Note Representations to Improve Mortality
Prediction | The 11th International Symposium on Digital Forensics and Security
(Full Paper, Oral Presentation) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Monitoring the health status of patients in the Intensive Care Unit (ICU) is
a critical aspect of providing superior care and treatment. The availability of
large-scale electronic health records (EHR) provides machine learning models
with an abundance of clinical text and vital sign data, enabling them to make
highly accurate predictions. Despite the emergence of advanced Natural Language
Processing (NLP) algorithms for clinical note analysis, the complex textual
structure and noise present in raw clinical data have posed significant
challenges. Coarse embedding approaches without domain-specific refinement have
limited the accuracy of these algorithms. To address this issue, we propose
FINEEHR, a system that utilizes two representation learning techniques, namely
metric learning and fine-tuning, to refine clinical note embeddings, while
leveraging the intrinsic correlations among different health statuses and note
categories. We evaluate the performance of FINEEHR using two metrics, namely
Area Under the Curve (AUC) and AUC-PR, on a real-world MIMIC III dataset. Our
experimental results demonstrate that both refinement approaches improve
prediction accuracy, and their combination yields the best results. Moreover,
our proposed method outperforms prior works, with an AUC improvement of over
10%, achieving an average AUC of 96.04% and an average AUC-PR of 96.48% across
various classifiers.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2023 02:42:52 GMT"
},
{
"version": "v2",
"created": "Thu, 4 May 2023 16:01:17 GMT"
}
] | 1,683,244,800,000 | [
[
"Wu",
"Jun",
""
],
[
"Ye",
"Xuesong",
""
],
[
"Mou",
"Chengjie",
""
],
[
"Dai",
"Weinan",
""
]
] |
2304.11823 | Mingli Zhu | Mingli Zhu, Shaokui Wei, Li Shen, Yanbo Fan, Baoyuan Wu | Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware
Minimization | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Backdoor defense, which aims to detect or mitigate the effect of malicious
triggers introduced by attackers, is becoming increasingly critical for machine
learning security and integrity. Fine-tuning based on benign data is a natural
defense to erase the backdoor effect in a backdoored model. However, recent
studies show that, given limited benign data, vanilla fine-tuning has poor
defense performance. In this work, we provide a deep study of fine-tuning the
backdoored model from the neuron perspective and find that backdoorrelated
neurons fail to escape the local minimum in the fine-tuning process. Inspired
by observing that the backdoorrelated neurons often have larger norms, we
propose FTSAM, a novel backdoor defense paradigm that aims to shrink the norms
of backdoor-related neurons by incorporating sharpness-aware minimization with
fine-tuning. We demonstrate the effectiveness of our method on several
benchmark datasets and network architectures, where it achieves
state-of-the-art defense performance. Overall, our work provides a promising
avenue for improving the robustness of machine learning models against backdoor
attacks.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2023 05:13:52 GMT"
}
] | 1,698,710,400,000 | [
[
"Zhu",
"Mingli",
""
],
[
"Wei",
"Shaokui",
""
],
[
"Shen",
"Li",
""
],
[
"Fan",
"Yanbo",
""
],
[
"Wu",
"Baoyuan",
""
]
] |
2304.11905 | Zhengchun Zhou | Yu-Xuan Zhang, Zhengchun Zhou, Xingxing He, Avik Ranjan Adhikary, and
Bapi Dutta | Data-driven Knowledge Fusion for Deep Multi-instance Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-instance learning (MIL) is a widely-applied technique in practical
applications that involve complex data structures. MIL can be broadly
categorized into two types: traditional methods and those based on deep
learning. These approaches have yielded significant results, especially with
regards to their problem-solving strategies and experimental validation,
providing valuable insights for researchers in the MIL field. However, a
considerable amount of knowledge is often trapped within the algorithm, leading
to subsequent MIL algorithms that solely rely on the model's data fitting to
predict unlabeled samples. This results in a significant loss of knowledge and
impedes the development of more intelligent models. In this paper, we propose a
novel data-driven knowledge fusion for deep multi-instance learning (DKMIL)
algorithm. DKMIL adopts a completely different idea from existing deep MIL
methods by analyzing the decision-making of key samples in the data set
(referred to as the data-driven) and using the knowledge fusion module designed
to extract valuable information from these samples to assist the model's
training. In other words, this module serves as a new interface between data
and the model, providing strong scalability and enabling the use of prior
knowledge from existing algorithms to enhance the learning ability of the
model. Furthermore, to adapt the downstream modules of the model to more
knowledge-enriched features extracted from the data-driven knowledge fusion
module, we propose a two-level attention module that gradually learns shallow-
and deep-level features of the samples to achieve more effective
classification. We will prove the scalability of the knowledge fusion module
while also verifying the efficacy of the proposed architecture by conducting
experiments on 38 data sets across 6 categories.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2023 08:28:51 GMT"
}
] | 1,682,380,800,000 | [
[
"Zhang",
"Yu-Xuan",
""
],
[
"Zhou",
"Zhengchun",
""
],
[
"He",
"Xingxing",
""
],
[
"Adhikary",
"Avik Ranjan",
""
],
[
"Dutta",
"Bapi",
""
]
] |
2304.11949 | Bo Xiong | Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui
Pan, Steffen Staab | Geometric Relational Embeddings: A Survey | Work in progress | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Geometric relational embeddings map relational data as geometric objects that
combine vector information suitable for machine learning and
structured/relational information for structured/relational reasoning,
typically in low dimensions. Their preservation of relational structures and
their appealing properties and interpretability have led to their uptake for
tasks such as knowledge graph completion, ontology and hierarchy reasoning,
logical query answering, and hierarchical multi-label classification. We survey
methods that underly geometric relational embeddings and categorize them based
on (i) the embedding geometries that are used to represent the data; and (ii)
the relational reasoning tasks that they aim to improve. We identify the
desired properties (i.e., inductive biases) of each kind of embedding and
discuss some potential future work.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2023 09:33:30 GMT"
}
] | 1,682,380,800,000 | [
[
"Xiong",
"Bo",
""
],
[
"Nayyeri",
"Mojtaba",
""
],
[
"Jin",
"Ming",
""
],
[
"He",
"Yunjie",
""
],
[
"Cochez",
"Michael",
""
],
[
"Pan",
"Shirui",
""
],
[
"Staab",
"Steffen",
""
]
] |
2304.12000 | Xianghua Zeng | Xianghua Zeng, Hao Peng, Angsheng Li, Chunyang Liu, Lifang He, Philip
S. Yu | Hierarchical State Abstraction Based on Structural Information
Principles | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | State abstraction optimizes decision-making by ignoring irrelevant
environmental information in reinforcement learning with rich observations.
Nevertheless, recent approaches focus on adequate representational capacities
resulting in essential information loss, affecting their performances on
challenging tasks. In this article, we propose a novel mathematical Structural
Information principles-based State Abstraction framework, namely SISA, from the
information-theoretic perspective. Specifically, an unsupervised, adaptive
hierarchical state clustering method without requiring manual assistance is
presented, and meanwhile, an optimal encoding tree is generated. On each
non-root tree node, a new aggregation function and condition structural entropy
are designed to achieve hierarchical state abstraction and compensate for
sampling-induced essential information loss in state abstraction. Empirical
evaluations on a visual gridworld domain and six continuous control benchmarks
demonstrate that, compared with five SOTA state abstraction approaches, SISA
significantly improves mean episode reward and sample efficiency up to 18.98
and 44.44%, respectively. Besides, we experimentally show that SISA is a
general framework that can be flexibly integrated with different
representation-learning objectives to improve their performances further.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2023 11:06:52 GMT"
}
] | 1,682,380,800,000 | [
[
"Zeng",
"Xianghua",
""
],
[
"Peng",
"Hao",
""
],
[
"Li",
"Angsheng",
""
],
[
"Liu",
"Chunyang",
""
],
[
"He",
"Lifang",
""
],
[
"Yu",
"Philip S.",
""
]
] |
2304.12090 | Xuejing Zheng | Chao Yu, Xuejing Zheng, Hankz Hankui Zhuo, Hai Wan, Weilin Luo | Reinforcement Learning with Knowledge Representation and Reasoning: A
Brief Survey | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement Learning(RL) has achieved tremendous development in recent
years, but still faces significant obstacles in addressing complex real-life
problems due to the issues of poor system generalization, low sample efficiency
as well as safety and interpretability concerns. The core reason underlying
such dilemmas can be attributed to the fact that most of the work has focused
on the computational aspect of value functions or policies using a
representational model to describe atomic components of rewards, states and
actions etc, thus neglecting the rich high-level declarative domain knowledge
of facts, relations and rules that can be either provided a priori or acquired
through reasoning over time. Recently, there has been a rapidly growing
interest in the use of Knowledge Representation and Reasoning(KRR) methods,
usually using logical languages, to enable more abstract representation and
efficient learning in RL. In this survey, we provide a preliminary overview on
these endeavors that leverage the strengths of KRR to help solving various
problems in RL, and discuss the challenging open problems and possible
directions for future work in this area.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2023 13:35:11 GMT"
}
] | 1,682,380,800,000 | [
[
"Yu",
"Chao",
""
],
[
"Zheng",
"Xuejing",
""
],
[
"Zhuo",
"Hankz Hankui",
""
],
[
"Wan",
"Hai",
""
],
[
"Luo",
"Weilin",
""
]
] |
2304.12146 | Olivier Goudet Dr | Cyril Grelier and Olivier Goudet and Jin-Kao Hao | Combining Monte Carlo Tree Search and Heuristic Search for Weighted
Vertex Coloring | arXiv admin note: substantial text overlap with arXiv:2202.01665 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This work investigates the Monte Carlo Tree Search (MCTS) method combined
with dedicated heuristics for solving the Weighted Vertex Coloring Problem. In
addition to the basic MCTS algorithm, we study several MCTS variants where the
conventional random simulation is replaced by other simulation strategies
including greedy and local search heuristics. We conduct experiments on
well-known benchmark instances to assess these combined MCTS variants. We
provide empirical evidence to shed light on the advantages and limits of each
simulation strategy. This is an extension of the work of Grelier and al.
presented at EvoCOP2022.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2023 14:50:33 GMT"
}
] | 1,682,380,800,000 | [
[
"Grelier",
"Cyril",
""
],
[
"Goudet",
"Olivier",
""
],
[
"Hao",
"Jin-Kao",
""
]
] |
2304.12479 | Ehsan Latif | Ehsan Latif, Gengchen Mai, Matthew Nyaaba, Xuansheng Wu, Ninghao Liu,
Guoyu Lu, Sheng Li, Tianming Liu, and Xiaoming Zhai | AGI: Artificial General Intelligence for Education | Position Paper on AGI for Education, Submitted to Technology and
Society | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial general intelligence (AGI) has gained global recognition as a
future technology due to the emergence of breakthrough large language models
and chatbots such as GPT-4 and ChatGPT, respectively. Compared to conventional
AI models, typically designed for a limited range of tasks, demand significant
amounts of domain-specific data for training and may not always consider
intricate interpersonal dynamics in education. AGI, driven by the recent large
pre-trained models, represents a significant leap in the capability of machines
to perform tasks that require human-level intelligence, such as reasoning,
problem-solving, decision-making, and even understanding human emotions and
social interactions. This position paper reviews AGI's key concepts,
capabilities, scope, and potential within future education, including achieving
future educational goals, designing pedagogy and curriculum, and performing
assessments. It highlights that AGI can significantly improve intelligent
tutoring systems, educational assessment, and evaluation procedures. AGI
systems can adapt to individual student needs, offering tailored learning
experiences. They can also provide comprehensive feedback on student
performance and dynamically adjust teaching methods based on student progress.
The paper emphasizes that AGI's capabilities extend to understanding human
emotions and social interactions, which are critical in educational settings.
The paper discusses that ethical issues in education with AGI include data
bias, fairness, and privacy and emphasizes the need for codes of conduct to
ensure responsible AGI use in academic settings like homework, teaching, and
recruitment. We also conclude that the development of AGI necessitates
interdisciplinary collaborations between educators and AI engineers to advance
research and application efforts.
| [
{
"version": "v1",
"created": "Mon, 24 Apr 2023 22:31:59 GMT"
},
{
"version": "v2",
"created": "Mon, 15 May 2023 12:14:49 GMT"
},
{
"version": "v3",
"created": "Mon, 30 Oct 2023 19:27:14 GMT"
},
{
"version": "v4",
"created": "Tue, 28 Nov 2023 17:26:51 GMT"
},
{
"version": "v5",
"created": "Wed, 13 Mar 2024 16:47:04 GMT"
}
] | 1,710,374,400,000 | [
[
"Latif",
"Ehsan",
""
],
[
"Mai",
"Gengchen",
""
],
[
"Nyaaba",
"Matthew",
""
],
[
"Wu",
"Xuansheng",
""
],
[
"Liu",
"Ninghao",
""
],
[
"Lu",
"Guoyu",
""
],
[
"Li",
"Sheng",
""
],
[
"Liu",
"Tianming",
""
],
[
"Zhai",
"Xiaoming",
""
]
] |
2304.12512 | Michael Sandborn | Henry Gilbert, Michael Sandborn, Douglas C. Schmidt, Jesse
Spencer-Smith, Jules White | Semantic Compression With Large Language Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rise of large language models (LLMs) is revolutionizing information
retrieval, question answering, summarization, and code generation tasks.
However, in addition to confidently presenting factually inaccurate information
at times (known as "hallucinations"), LLMs are also inherently limited by the
number of input and output tokens that can be processed at once, making them
potentially less effective on tasks that require processing a large set or
continuous stream of information. A common approach to reducing the size of
data is through lossless or lossy compression. Yet, in some cases it may not be
strictly necessary to perfectly recover every detail from the original data, as
long as a requisite level of semantic precision or intent is conveyed.
This paper presents three contributions to research on LLMs. First, we
present the results from experiments exploring the viability of approximate
compression using LLMs, focusing specifically on GPT-3.5 and GPT-4 via ChatGPT
interfaces. Second, we investigate and quantify the capability of LLMs to
compress text and code, as well as to recall and manipulate compressed
representations of prompts. Third, we present two novel metrics -- Exact
Reconstructive Effectiveness (ERE) and Semantic Reconstruction Effectiveness
(SRE) -- that quantify the level of preserved intent between text compressed
and decompressed by the LLMs we studied. Our initial results indicate that
GPT-4 can effectively compress and reconstruct text while preserving the
semantic essence of the original text, providing a path to leverage
$\sim$5$\times$ more tokens than present limits allow.
| [
{
"version": "v1",
"created": "Tue, 25 Apr 2023 01:47:05 GMT"
}
] | 1,682,467,200,000 | [
[
"Gilbert",
"Henry",
""
],
[
"Sandborn",
"Michael",
""
],
[
"Schmidt",
"Douglas C.",
""
],
[
"Spencer-Smith",
"Jesse",
""
],
[
"White",
"Jules",
""
]
] |
2304.12604 | Hao Dong | Hao Dong, Zhiyuan Ning, Pengyang Wang, Ziyue Qiao, Pengfei Wang,
Yuanchun Zhou, Yanjie Fu | Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning | Accepted to IJCAI 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal knowledge graph (TKG) reasoning aims to predict the future missing
facts based on historical information and has gained increasing research
interest recently. Lots of works have been made to model the historical
structural and temporal characteristics for the reasoning task. Most existing
works model the graph structure mainly depending on entity representation.
However, the magnitude of TKG entities in real-world scenarios is considerable,
and an increasing number of new entities will arise as time goes on. Therefore,
we propose a novel architecture modeling with relation feature of TKG, namely
aDAptivE path-MemOry Network (DaeMon), which adaptively models the temporal
path information between query subject and each object candidate across history
time. It models the historical information without depending on entity
representation. Specifically, DaeMon uses path memory to record the temporal
path information derived from path aggregation unit across timeline considering
the memory passing strategy between adjacent timestamps. Extensive experiments
conducted on four real-world TKG datasets demonstrate that our proposed model
obtains substantial performance improvement and outperforms the
state-of-the-art up to 4.8% absolute in MRR.
| [
{
"version": "v1",
"created": "Tue, 25 Apr 2023 06:33:08 GMT"
}
] | 1,682,467,200,000 | [
[
"Dong",
"Hao",
""
],
[
"Ning",
"Zhiyuan",
""
],
[
"Wang",
"Pengyang",
""
],
[
"Qiao",
"Ziyue",
""
],
[
"Wang",
"Pengfei",
""
],
[
"Zhou",
"Yuanchun",
""
],
[
"Fu",
"Yanjie",
""
]
] |
2304.12653 | Min Yang | Min Yang, Guanjun Liu, Ziyuan Zhou | Partially Observable Mean Field Multi-Agent Reinforcement Learning Based
on Graph-Attention | null | Drones 2023, 7(7), 476 | 10.3390/drones7070476 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Traditional multi-agent reinforcement learning algorithms are difficultly
applied in a large-scale multi-agent environment. The introduction of mean
field theory has enhanced the scalability of multi-agent reinforcement learning
in recent years. This paper considers partially observable multi-agent
reinforcement learning (MARL), where each agent can only observe other agents
within a fixed range. This partial observability affects the agent's ability to
assess the quality of the actions of surrounding agents. This paper focuses on
developing a method to capture more effective information from local
observations in order to select more effective actions. Previous work in this
field employs probability distributions or weighted mean field to update the
average actions of neighborhood agents, but it does not fully consider the
feature information of surrounding neighbors and leads to a local optimum. In
this paper, we propose a novel multi-agent reinforcement learning algorithm,
Partially Observable Mean Field Multi-Agent Reinforcement Learning based on
Graph--Attention (GAMFQ) to remedy this flaw. GAMFQ uses a graph attention
module and a mean field module to describe how an agent is influenced by the
actions of other agents at each time step. This graph attention module consists
of a graph attention encoder and a differentiable attention mechanism, and this
mechanism outputs a dynamic graph to represent the effectiveness of
neighborhood agents against central agents. The mean--field module approximates
the effect of a neighborhood agent on a central agent as the average effect of
effective neighborhood agents. We evaluate GAMFQ on three challenging tasks in
the MAgents framework. Experiments show that GAMFQ outperforms baselines
including the state-of-the-art partially observable mean-field reinforcement
learning algorithms.
| [
{
"version": "v1",
"created": "Tue, 25 Apr 2023 08:38:32 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Mar 2024 06:25:06 GMT"
}
] | 1,709,683,200,000 | [
[
"Yang",
"Min",
""
],
[
"Liu",
"Guanjun",
""
],
[
"Zhou",
"Ziyuan",
""
]
] |
2304.12667 | Dieter Brughmans | Dieter Brughmans, Lissa Melis, David Martens | Disagreement amongst counterfactual explanations: How transparency can
be deceptive | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Counterfactual explanations are increasingly used as an Explainable
Artificial Intelligence (XAI) technique to provide stakeholders of complex
machine learning algorithms with explanations for data-driven decisions. The
popularity of counterfactual explanations resulted in a boom in the algorithms
generating them. However, not every algorithm creates uniform explanations for
the same instance. Even though in some contexts multiple possible explanations
are beneficial, there are circumstances where diversity amongst counterfactual
explanations results in a potential disagreement problem among stakeholders.
Ethical issues arise when for example, malicious agents use this diversity to
fairwash an unfair machine learning model by hiding sensitive features. As
legislators worldwide tend to start including the right to explanations for
data-driven, high-stakes decisions in their policies, these ethical issues
should be understood and addressed. Our literature review on the disagreement
problem in XAI reveals that this problem has never been empirically assessed
for counterfactual explanations. Therefore, in this work, we conduct a
large-scale empirical analysis, on 40 datasets, using 12 explanation-generating
methods, for two black-box models, yielding over 192.0000 explanations. Our
study finds alarmingly high disagreement levels between the methods tested. A
malicious user is able to both exclude and include desired features when
multiple counterfactual explanations are available. This disagreement seems to
be driven mainly by the dataset characteristics and the type of counterfactual
algorithm. XAI centers on the transparency of algorithmic decision-making, but
our analysis advocates for transparency about this self-proclaimed transparency
| [
{
"version": "v1",
"created": "Tue, 25 Apr 2023 09:15:37 GMT"
}
] | 1,682,467,200,000 | [
[
"Brughmans",
"Dieter",
""
],
[
"Melis",
"Lissa",
""
],
[
"Martens",
"David",
""
]
] |
2304.12686 | Michael Timothy Bennett | Michael Timothy Bennett | On the Computation of Meaning, Language Models and Incomprehensible
Horrors | Published (and accepted for full oral presentation) at the 16th
Conference on Artificial General Intelligence, Stockholm, 2023 | Proceedings of the 16th International Conference on Artificial
General Intelligence. 2023. Lecture Notes in Computer Science, vol 13921.
Springer. pp. 32-41 | 10.1007/978-3-031-33469-6_4 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We integrate foundational theories of meaning with a mathematical formalism
of artificial general intelligence (AGI) to offer a comprehensive mechanistic
explanation of meaning, communication, and symbol emergence. This synthesis
holds significance for both AGI and broader debates concerning the nature of
language, as it unifies pragmatics, logical truth conditional semantics,
Peircean semiotics, and a computable model of enactive cognition, addressing
phenomena that have traditionally evaded mechanistic explanation. By examining
the conditions under which a machine can generate meaningful utterances or
comprehend human meaning, we establish that the current generation of language
models do not possess the same understanding of meaning as humans nor intend
any meaning that we might attribute to their responses. To address this, we
propose simulating human feelings and optimising models to construct weak
representations. Our findings shed light on the relationship between meaning
and intelligence, and how we can build machines that comprehend and intend
meaning.
| [
{
"version": "v1",
"created": "Tue, 25 Apr 2023 09:41:00 GMT"
},
{
"version": "v2",
"created": "Thu, 11 Apr 2024 04:41:25 GMT"
}
] | 1,712,880,000,000 | [
[
"Bennett",
"Michael Timothy",
""
]
] |
2304.12828 | Xin Su | Kuo Yang, Zecong Yu, Xin Su, Xiong He, Ning Wang, Qiguang Zheng,
Feidie Yu, Zhuang Liu, Tiancai Wen and Xuezhong Zhou | A optimization framework for herbal prescription planning based on deep
reinforcement learning | 13 pages, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Treatment planning for chronic diseases is a critical task in medical
artificial intelligence, particularly in traditional Chinese medicine (TCM).
However, generating optimized sequential treatment strategies for patients with
chronic diseases in different clinical encounters remains a challenging issue
that requires further exploration. In this study, we proposed a TCM herbal
prescription planning framework based on deep reinforcement learning for
chronic disease treatment (PrescDRL). PrescDRL is a sequential herbal
prescription optimization model that focuses on long-term effectiveness rather
than achieving maximum reward at every step, thereby ensuring better patient
outcomes. We constructed a high-quality benchmark dataset for sequential
diagnosis and treatment of diabetes and evaluated PrescDRL against this
benchmark. Our results showed that PrescDRL achieved a higher curative effect,
with the single-step reward improving by 117% and 153% compared to doctors.
Furthermore, PrescDRL outperformed the benchmark in prescription prediction,
with precision improving by 40.5% and recall improving by 63%. Overall, our
study demonstrates the potential of using artificial intelligence to improve
clinical intelligent diagnosis and treatment in TCM.
| [
{
"version": "v1",
"created": "Tue, 25 Apr 2023 13:55:02 GMT"
}
] | 1,682,467,200,000 | [
[
"Yang",
"Kuo",
""
],
[
"Yu",
"Zecong",
""
],
[
"Su",
"Xin",
""
],
[
"He",
"Xiong",
""
],
[
"Wang",
"Ning",
""
],
[
"Zheng",
"Qiguang",
""
],
[
"Yu",
"Feidie",
""
],
[
"Liu",
"Zhuang",
""
],
[
"Wen",
"Tiancai",
""
],
[
"Zhou",
"Xuezhong",
""
]
] |
2304.13269 | Chengpeng Hu | Chengpeng Hu, Yunlong Zhao, Ziqi Wang, Haocheng Du, Jialin Liu | Games for Artificial Intelligence Research: A Review and Perspectives | This paper has been accepted by IEEE Transactions on Artificial
Intelligence | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Games have been the perfect test-beds for artificial intelligence research
for the characteristics that widely exist in real-world scenarios. Learning and
optimisation, decision making in dynamic and uncertain environments, game
theory, planning and scheduling, design and education are common research areas
shared between games and real-world problems. Numerous open-source games or
game-based environments have been implemented for studying artificial
intelligence. In addition to single- or multi-player, collaborative or
adversarial games, there has also been growing interest in implementing
platforms for creative design in recent years. Those platforms provide ideal
benchmarks for exploring and comparing artificial intelligence ideas and
techniques. This paper reviews the games and game-based platforms for
artificial intelligence research, provides guidance on matching particular
types of artificial intelligence with suitable games for testing and matching
particular needs in games with suitable artificial intelligence techniques,
discusses the research trend induced by the evolution of those games and
platforms, and gives an outlook.
| [
{
"version": "v1",
"created": "Wed, 26 Apr 2023 03:42:31 GMT"
},
{
"version": "v2",
"created": "Thu, 25 May 2023 13:17:51 GMT"
},
{
"version": "v3",
"created": "Tue, 5 Mar 2024 05:40:48 GMT"
},
{
"version": "v4",
"created": "Tue, 4 Jun 2024 05:18:04 GMT"
}
] | 1,717,545,600,000 | [
[
"Hu",
"Chengpeng",
""
],
[
"Zhao",
"Yunlong",
""
],
[
"Wang",
"Ziqi",
""
],
[
"Du",
"Haocheng",
""
],
[
"Liu",
"Jialin",
""
]
] |
2304.13626 | Daniel Silver Dr. | Daniel L. Silver and Tom M. Mitchell | The Roles of Symbols in Neural-based AI: They are Not What You Think! | 28 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose that symbols are first and foremost external communication tools
used between intelligent agents that allow knowledge to be transferred in a
more efficient and effective manner than having to experience the world
directly. But, they are also used internally within an agent through a form of
self-communication to help formulate, describe and justify subsymbolic patterns
of neural activity that truly implement thinking. Symbols, and our languages
that make use of them, not only allow us to explain our thinking to others and
ourselves, but also provide beneficial constraints (inductive bias) on learning
about the world. In this paper we present relevant insights from neuroscience
and cognitive science, about how the human brain represents symbols and the
concepts they refer to, and how today's artificial neural networks can do the
same. We then present a novel neuro-symbolic hypothesis and a plausible
architecture for intelligent agents that combines subsymbolic representations
for symbols and concepts for learning and reasoning. Our hypothesis and
associated architecture imply that symbols will remain critical to the future
of intelligent systems NOT because they are the fundamental building blocks of
thought, but because they are characterizations of subsymbolic processes that
constitute thought.
| [
{
"version": "v1",
"created": "Wed, 26 Apr 2023 15:33:41 GMT"
}
] | 1,682,553,600,000 | [
[
"Silver",
"Daniel L.",
""
],
[
"Mitchell",
"Tom M.",
""
]
] |
2304.13688 | Tobias M\"uller | Tobias M\"uller, Milena Zahn and Florian Matthes | Unlocking the Potential of Collaborative AI -- On the Socio-technical
Challenges of Federated Machine Learning | Accepted for Publication at the 31st European Conference on
Information Systems (ECIS 2023) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The disruptive potential of AI systems roots in the emergence of big data.
Yet, a significant portion is scattered and locked in data silos, leaving its
potential untapped. Federated Machine Learning is a novel AI paradigm enabling
the creation of AI models from decentralized, potentially siloed data. Hence,
Federated Machine Learning could technically open data silos and therefore
unlock economic potential. However, this requires collaboration between
multiple parties owning data silos. Setting up collaborative business models is
complex and often a reason for failure. Current literature lacks guidelines on
which aspects must be considered to successfully realize collaborative AI
projects. This research investigates the challenges of prevailing collaborative
business models and distinct aspects of Federated Machine Learning. Through a
systematic literature review, focus group, and expert interviews, we provide a
systemized collection of socio-technical challenges and an extended Business
Model Canvas for the initial viability assessment of collaborative AI projects.
| [
{
"version": "v1",
"created": "Wed, 26 Apr 2023 17:14:44 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Apr 2023 07:47:17 GMT"
},
{
"version": "v3",
"created": "Sat, 29 Apr 2023 00:15:47 GMT"
}
] | 1,682,985,600,000 | [
[
"Müller",
"Tobias",
""
],
[
"Zahn",
"Milena",
""
],
[
"Matthes",
"Florian",
""
]
] |
2304.13765 | Alexis Roger | Alexis Roger, Esma A\"imeur, Irina Rish | Towards ethical multimodal systems | 5 pages, multimodal ethical dataset building, accepted in the NeurIPS
2023 MP2 workshop | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Generative AI systems (ChatGPT, DALL-E, etc) are expanding into multiple
areas of our lives, from art Rombach et al. [2021] to mental health Rob Morris
and Kareem Kouddous [2022]; their rapidly growing societal impact opens new
opportunities, but also raises ethical concerns. The emerging field of AI
alignment aims to make AI systems reflect human values. This paper focuses on
evaluating the ethics of multimodal AI systems involving both text and images -
a relatively under-explored area, as most alignment work is currently focused
on language models. We first create a multimodal ethical database from human
feedback on ethicality. Then, using this database, we develop algorithms,
including a RoBERTa-large classifier and a multilayer perceptron, to
automatically assess the ethicality of system responses.
| [
{
"version": "v1",
"created": "Wed, 26 Apr 2023 18:11:33 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Nov 2023 15:19:07 GMT"
},
{
"version": "v3",
"created": "Mon, 20 May 2024 08:29:33 GMT"
}
] | 1,716,249,600,000 | [
[
"Roger",
"Alexis",
""
],
[
"Aïmeur",
"Esma",
""
],
[
"Rish",
"Irina",
""
]
] |
2304.13854 | Mingchen Li | Mingchen Li and Lifu Huang | Understand the Dynamic World: An End-to-End Knowledge Informed Framework
for Open Domain Entity State Tracking | Published as a conference paper at SIGIR 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Open domain entity state tracking aims to predict reasonable state changes of
entities (i.e., [attribute] of [entity] was [before_state] and [after_state]
afterwards) given the action descriptions. It's important to many reasoning
tasks to support human everyday activities. However, it's challenging as the
model needs to predict an arbitrary number of entity state changes caused by
the action while most of the entities are implicitly relevant to the actions
and their attributes as well as states are from open vocabularies. To tackle
these challenges, we propose a novel end-to-end Knowledge Informed framework
for open domain Entity State Tracking, namely KIEST, which explicitly retrieves
the relevant entities and attributes from external knowledge graph (i.e.,
ConceptNet) and incorporates them to autoregressively generate all the entity
state changes with a novel dynamic knowledge grained encoder-decoder framework.
To enforce the logical coherence among the predicted entities, attributes, and
states, we design a new constraint decoding strategy and employ a coherence
reward to improve the decoding process. Experimental results show that our
proposed KIEST framework significantly outperforms the strong baselines on the
public benchmark dataset OpenPI.
| [
{
"version": "v1",
"created": "Wed, 26 Apr 2023 22:45:30 GMT"
}
] | 1,682,640,000,000 | [
[
"Li",
"Mingchen",
""
],
[
"Huang",
"Lifu",
""
]
] |
2304.13911 | Xiangyang Liu | Xiangyang Liu, Tianqi Pang, Chenyou Fan | Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs
Answering | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate how to enhance answer precision in frequently asked questions
posed by distributed users using cloud-based Large Language Models (LLMs). Our
study focuses on a typical situations where users ask similar queries that
involve identical mathematical reasoning steps and problem-solving procedures.
Due to the unsatisfactory accuracy of LLMs' zero-shot prompting with standalone
questions, we propose to improve the distributed synonymous questions using
Self-Consistency (SC) and Chain-of-Thought (CoT) techniques. Specifically, we
first retrieve synonymous questions from a crowd-sourced database and create a
federated question pool. We call these federated synonymous questions with the
same or different parameters SP-questions or DP-questions, respectively. We
refer to our methods as Fed-SP-SC and Fed-DP-CoT, which can generate
significantly more accurate answers for all user queries without requiring
sophisticated model-tuning. Through extensive experiments, we demonstrate that
our proposed methods can significantly enhance question accuracy by fully
exploring the synonymous nature of the questions and the consistency of the
answers.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2023 01:48:03 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Jun 2023 13:21:36 GMT"
}
] | 1,688,342,400,000 | [
[
"Liu",
"Xiangyang",
""
],
[
"Pang",
"Tianqi",
""
],
[
"Fan",
"Chenyou",
""
]
] |
2304.13922 | Colan Biemer | Colan F. Biemer and Seth Cooper | Level Assembly as a Markov Decision Process | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many games feature a progression of levels that doesn't adapt to the player.
This can be problematic because some players may get stuck if the progression
is too difficult, while others may find it boring if the progression is too
slow to get to more challenging levels. This can be addressed by building
levels based on the player's performance and preferences. In this work, we
formulate the problem of generating levels for a player as a Markov Decision
Process (MDP) and use adaptive dynamic programming (ADP) to solve the MDP
before assembling a level. We tested with two case studies and found that using
an ADP outperforms two baselines. Furthermore, we experimented with player
proxies and switched them in the middle of play, and we show that a simple
modification prior to running ADP results in quick adaptation. By using ADP,
which searches the entire MDP, we produce a dynamic progression of levels that
adapts to the player.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2023 02:15:16 GMT"
}
] | 1,682,640,000,000 | [
[
"Biemer",
"Colan F.",
""
],
[
"Cooper",
"Seth",
""
]
] |
2304.13998 | Tung Nguyen Thanh | Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Kashyap, Stefan Winkler,
Shao-Syuan Huang, Jie-Jyun Liu, Chih-Jen Lin | Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification | Benchmark, Multilabel, Classification | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clinical notes are assigned ICD codes - sets of codes for diagnoses and
procedures. In the recent years, predictive machine learning models have been
built for automatic ICD coding. However, there is a lack of widely accepted
benchmarks for automated ICD coding models based on large-scale public EHR
data.
This paper proposes a public benchmark suite for ICD-10 coding using a large
EHR dataset derived from MIMIC-IV, the most recent public EHR dataset. We
implement and compare several popular methods for ICD coding prediction tasks
to standardize data preprocessing and establish a comprehensive ICD coding
benchmark dataset. This approach fosters reproducibility and model comparison,
accelerating progress toward employing automated ICD coding in future studies.
Furthermore, we create a new ICD-9 benchmark using MIMIC-IV data, providing
more data points and a higher number of ICD codes than MIMIC-III. Our
open-source code offers easy access to data processing steps, benchmark
creation, and experiment replication for those with MIMIC-IV access, providing
insights, guidance, and protocols to efficiently develop ICD coding models.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2023 07:36:14 GMT"
}
] | 1,682,640,000,000 | [
[
"Nguyen",
"Thanh-Tung",
""
],
[
"Schlegel",
"Viktor",
""
],
[
"Kashyap",
"Abhinav",
""
],
[
"Winkler",
"Stefan",
""
],
[
"Huang",
"Shao-Syuan",
""
],
[
"Liu",
"Jie-Jyun",
""
],
[
"Lin",
"Chih-Jen",
""
]
] |
2304.14115 | Junlin Lu | Junlin Lu, Patrick Mannion, Karl Mason | Inferring Preferences from Demonstrations in Multi-objective
Reinforcement Learning: A Dynamic Weight-based Approach | This work is accepted by ALA 2023 Adaptive and Learning Agents
Workshop at AAMAS, London, UK | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Many decision-making problems feature multiple objectives. In such problems,
it is not always possible to know the preferences of a decision-maker for
different objectives. However, it is often possible to observe the behavior of
decision-makers. In multi-objective decision-making, preference inference is
the process of inferring the preferences of a decision-maker for different
objectives. This research proposes a Dynamic Weight-based Preference Inference
(DWPI) algorithm that can infer the preferences of agents acting in
multi-objective decision-making problems, based on observed behavior
trajectories in the environment. The proposed method is evaluated on three
multi-objective Markov decision processes: Deep Sea Treasure, Traffic, and Item
Gathering. The performance of the proposed DWPI approach is compared to two
existing preference inference methods from the literature, and empirical
results demonstrate significant improvements compared to the baseline
algorithms, in terms of both time requirements and accuracy of the inferred
preferences. The Dynamic Weight-based Preference Inference algorithm also
maintains its performance when inferring preferences for sub-optimal behavior
demonstrations. In addition to its impressive performance, the Dynamic
Weight-based Preference Inference algorithm does not require any interactions
during training with the agent whose preferences are inferred, all that is
required is a trajectory of observed behavior.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2023 11:55:07 GMT"
}
] | 1,682,640,000,000 | [
[
"Lu",
"Junlin",
""
],
[
"Mannion",
"Patrick",
""
],
[
"Mason",
"Karl",
""
]
] |
2304.14130 | Ioannis Papantonis | Ioannis Papantonis, Vaishak Belle | Why not both? Complementing explanations with uncertainty, and the role
of self-confidence in Human-AI collaboration | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | AI and ML models have already found many applications in critical domains,
such as healthcare and criminal justice. However, fully automating such
high-stakes applications can raise ethical or fairness concerns. Instead, in
such cases, humans should be assisted by automated systems so that the two
parties reach a joint decision, stemming out of their interaction. In this work
we conduct an empirical study to identify how uncertainty estimates and model
explanations affect users' reliance, understanding, and trust towards a model,
looking for potential benefits of bringing the two together. Moreover, we seek
to assess how users' behaviour is affected by their own self-confidence in
their abilities to perform a certain task, while we also discuss how the latter
may distort the outcome of an analysis based on agreement and switching
percentages.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2023 12:24:33 GMT"
}
] | 1,682,640,000,000 | [
[
"Papantonis",
"Ioannis",
""
],
[
"Belle",
"Vaishak",
""
]
] |
2304.14243 | Luc\'ia G\'omez \'Alvarez | Nicola Gigante, Lucia {Gomez Alvarez}, Tim S. Lyon | Standpoint Linear Temporal Logic | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Many complex scenarios require the coordination of agents possessing unique
points of view and distinct semantic commitments. In response, standpoint logic
(SL) was introduced in the context of knowledge integration, allowing one to
reason with diverse and potentially conflicting viewpoints by means of indexed
modalities. Another multi-modal logic of import is linear temporal logic (LTL)
- a formalism used to express temporal properties of systems and processes,
having prominence in formal methods and fields related to artificial
intelligence. In this paper, we present standpoint linear temporal logic
(SLTL), a new logic that combines the temporal features of LTL with the
multi-perspective modelling capacity of SL. We define the logic SLTL, its
syntax, and its semantics, establish its decidability and complexity, and
provide a terminating tableau calculus to automate SLTL reasoning.
Conveniently, this offers a clear path to extend existing LTL reasoners with
practical reasoning support for temporal reasoning in multi-perspective
settings.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2023 15:03:38 GMT"
}
] | 1,682,640,000,000 | [
[
"Gigante",
"Nicola",
""
],
[
"Alvarez}",
"Lucia {Gomez",
""
],
[
"Lyon",
"Tim S.",
""
]
] |
2304.14323 | Luc\'ia G\'omez \'Alvarez | Luc\'ia G\'omez \'Alvarez, Sebastian Rudolph and Hannes Strass | Pushing the Boundaries of Tractable Multiperspective Reasoning: A
Deduction Calculus for Standpoint EL+ | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Standpoint EL is a multi-modal extension of the popular description logic EL
that allows for the integrated representation of domain knowledge relative to
diverse standpoints or perspectives. Advantageously, its satisfiability problem
has recently been shown to be in PTime, making it a promising framework for
large-scale knowledge integration.
In this paper, we show that we can further push the expressivity of this
formalism, arriving at an extended logic, called Standpoint EL+, which allows
for axiom negation, role chain axioms, self-loops, and other features, while
maintaining tractability. This is achieved by designing a
satisfiability-checking deduction calculus, which at the same time addresses
the need for practical algorithms. We demonstrate the feasibility of our
calculus by presenting a prototypical Datalog implementation of its deduction
rules.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2023 16:49:17 GMT"
},
{
"version": "v2",
"created": "Thu, 11 May 2023 15:55:56 GMT"
}
] | 1,683,849,600,000 | [
[
"Álvarez",
"Lucía Gómez",
""
],
[
"Rudolph",
"Sebastian",
""
],
[
"Strass",
"Hannes",
""
]
] |
2304.14334 | Suleyman Olcay Polat | Solomon Ubani, Suleyman Olcay Polat, Rodney Nielsen | ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we investigate the use of data obtained from prompting a large
generative language model, ChatGPT, to generate synthetic training data with
the aim of augmenting data in low resource scenarios. We show that with
appropriate task-specific ChatGPT prompts, we outperform the most popular
existing approaches for such data augmentation. Furthermore, we investigate
methodologies for evaluating the similarity of the augmented data generated
from ChatGPT with the aim of validating and assessing the quality of the data
generated.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2023 17:07:29 GMT"
}
] | 1,682,640,000,000 | [
[
"Ubani",
"Solomon",
""
],
[
"Polat",
"Suleyman Olcay",
""
],
[
"Nielsen",
"Rodney",
""
]
] |
2304.14531 | Tianyi Huang | Tianyi Huang, Shenghui Cheng, Stan Z. Li, Zhengjun Zhang | High-dimensional Clustering onto Hamiltonian Cycle | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Clustering aims to group unlabelled samples based on their similarities. It
has become a significant tool for the analysis of high-dimensional data.
However, most of the clustering methods merely generate pseudo labels and thus
are unable to simultaneously present the similarities between different
clusters and outliers. This paper proposes a new framework called
High-dimensional Clustering onto Hamiltonian Cycle (HCHC) to solve the above
problems. First, HCHC combines global structure with local structure in one
objective function for deep clustering, improving the labels as relative
probabilities, to mine the similarities between different clusters while
keeping the local structure in each cluster. Then, the anchors of different
clusters are sorted on the optimal Hamiltonian cycle generated by the cluster
similarities and mapped on the circumference of a circle. Finally, a sample
with a higher probability of a cluster will be mapped closer to the
corresponding anchor. In this way, our framework allows us to appreciate three
aspects visually and simultaneously - clusters (formed by samples with high
probabilities), cluster similarities (represented as circular distances), and
outliers (recognized as dots far away from all clusters). The experiments
illustrate the superiority of HCHC.
| [
{
"version": "v1",
"created": "Thu, 27 Apr 2023 20:59:45 GMT"
},
{
"version": "v2",
"created": "Sat, 17 Jun 2023 14:11:47 GMT"
}
] | 1,687,305,600,000 | [
[
"Huang",
"Tianyi",
""
],
[
"Cheng",
"Shenghui",
""
],
[
"Li",
"Stan Z.",
""
],
[
"Zhang",
"Zhengjun",
""
]
] |
2304.14583 | Ishan Bangroo Sr. | Ishan Shivansh Bangroo, Samia Tahzeen | \'Epilexie: A digital therapeutic approach for treating intractable
epilepsy via Amenable Neurostimulation | 8 pages,3 figures,3 tables | International Journal Dental and Medical Sciences Research, Volume
5, Issue 2, Mar - Apr 2023 pp 327-334 | 10.35629/5252-0502327334 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Epilepsy is a neurological illness that is characterised by continuous spasms
of shaking, sometimes known as convulsions. Although there are effective
treatments for epilepsy, such as drugs and surgery, there is still a group of
individuals who have intractable epilepsy that fails to respond to standard
methods. Intractable epilepsy is a severe neurological illness that ripples
across the globe and impacts millions of individuals. It is extremely difficult
to control intractable epilepsy, which is defined as the lack of response to
two or more standard antiepileptic medication treatments. In recent years, the
use of programmable electrical stimulation of the brain has shown promise as a
digital treatment strategy for lowering seizure frequency in individuals with
intractable epilepsy. In this research, the use of Amenable Neurostimulation
(ANS) as part of a digital treatment strategy to intractable epilepsy is
investigated. When applied to the brain, ANS uses a closed-loop system to
selectively stimulate neurons in the affected areas, therefore lowering the
frequency of seizures. In addition, the report describes the design and
execution of a pilot research employing ANS to treat intractable epilepsy,
including patient selection criteria, device settings, and outcome measures.
The findings of this pilot research point to the possibility that ANS might be
a realistic and successful therapy option for people afflicted with intractable
epilepsy. This paper demonstrated the prospects of digital medicines in
treating complicated neurological illnesses and recommends future routes for
research and development in this field.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 01:06:15 GMT"
}
] | 1,682,899,200,000 | [
[
"Bangroo",
"Ishan Shivansh",
""
],
[
"Tahzeen",
"Samia",
""
]
] |
2304.14635 | Jie Liu | Jie Liu, Mengting He, Guangtao Wang, Nguyen Quoc Viet Hung, Xuequn
Shang, Hongzhi Yin | Imbalanced Node Classification Beyond Homophilic Assumption | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Imbalanced node classification widely exists in real-world networks where
graph neural networks (GNNs) are usually highly inclined to majority classes
and suffer from severe performance degradation on classifying minority class
nodes. Various imbalanced node classification methods have been proposed
recently which construct synthetic nodes and edges w.r.t. minority classes to
balance the label and topology distribution. However, they are all based on the
homophilic assumption that nodes of the same label tend to connect despite the
wide existence of heterophilic edges in real-world graphs. Thus, they uniformly
aggregate features from both homophilic and heterophilic neighbors and rely on
feature similarity to generate synthetic edges, which cannot be applied to
imbalanced graphs in high heterophily. To address this problem, we propose a
novel GraphSANN for imbalanced node classification on both homophilic and
heterophilic graphs. Firstly, we propose a unified feature mixer to generate
synthetic nodes with both homophilic and heterophilic interpolation in a
unified way. Next, by randomly sampling edges between synthetic nodes and
existing nodes as candidate edges, we design an adaptive subgraph extractor to
adaptively extract the contextual subgraphs of candidate edges with flexible
ranges. Finally, we develop a multi-filter subgraph encoder that constructs
different filter channels to discriminatively aggregate neighbor's information
along the homophilic and heterophilic edges. Extensive experiments on eight
datasets demonstrate the superiority of our model for imbalanced node
classification on both homophilic and heterophilic graphs.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 05:33:19 GMT"
}
] | 1,682,899,200,000 | [
[
"Liu",
"Jie",
""
],
[
"He",
"Mengting",
""
],
[
"Wang",
"Guangtao",
""
],
[
"Hung",
"Nguyen Quoc Viet",
""
],
[
"Shang",
"Xuequn",
""
],
[
"Yin",
"Hongzhi",
""
]
] |
2304.14659 | Alexandre Quemy | Alexandre Quemy, Marc Schoenauer, Johann Dreo | MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with
Known Pareto Front | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Multi-objective AI planning suffers from a lack of benchmarks exhibiting
known Pareto Fronts. In this work, we propose a tunable benchmark generator,
together with a dedicated solver that provably computes the true Pareto front
of the resulting instances. First, we prove a proposition allowing us to
characterize the optimal plans for a constrained version of the problem, and
then show how to reduce the general problem to the constrained one. Second, we
provide a constructive way to find all the Pareto-optimal plans and discuss the
complexity of the algorithm. We provide an implementation that allows the
solver to handle realistic instances in a reasonable time. Finally, as a
practical demonstration, we used this solver to find all Pareto-optimal plans
between the two largest airports in the world, considering the routes between
the 50 largest airports, spherical distances between airports and a made-up
risk.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 07:09:23 GMT"
}
] | 1,682,899,200,000 | [
[
"Quemy",
"Alexandre",
""
],
[
"Schoenauer",
"Marc",
""
],
[
"Dreo",
"Johann",
""
]
] |
2304.14670 | Enze Shi | Jiaqi Wang, Enze Shi, Sigang Yu, Zihao Wu, Chong Ma, Haixing Dai,
Qiushi Yang, Yanqing Kang, Jinru Wu, Huawen Hu, Chenxi Yue, Haiyang Zhang,
Yiheng Liu, Yi Pan, Zhengliang Liu, Lichao Sun, Xiang Li, Bao Ge, Xi Jiang,
Dajiang Zhu, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang | Prompt Engineering for Healthcare: Methodologies and Applications | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Prompt engineering is a critical technique in the field of natural language
processing that involves designing and optimizing the prompts used to input
information into models, aiming to enhance their performance on specific tasks.
With the recent advancements in large language models, prompt engineering has
shown significant superiority across various domains and has become
increasingly important in the healthcare domain. However, there is a lack of
comprehensive reviews specifically focusing on prompt engineering in the
medical field. This review will introduce the latest advances in prompt
engineering in the field of natural language processing for the medical field.
First, we will provide the development of prompt engineering and emphasize its
significant contributions to healthcare natural language processing
applications such as question-answering systems, text summarization, and
machine translation. With the continuous improvement of general large language
models, the importance of prompt engineering in the healthcare domain is
becoming increasingly prominent. The aim of this article is to provide useful
resources and bridges for healthcare natural language processing researchers to
better explore the application of prompt engineering in this field. We hope
that this review can provide new ideas and inspire for research and application
in medical natural language processing.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 08:03:42 GMT"
},
{
"version": "v2",
"created": "Sat, 23 Mar 2024 10:10:18 GMT"
}
] | 1,711,411,200,000 | [
[
"Wang",
"Jiaqi",
""
],
[
"Shi",
"Enze",
""
],
[
"Yu",
"Sigang",
""
],
[
"Wu",
"Zihao",
""
],
[
"Ma",
"Chong",
""
],
[
"Dai",
"Haixing",
""
],
[
"Yang",
"Qiushi",
""
],
[
"Kang",
"Yanqing",
""
],
[
"Wu",
"Jinru",
""
],
[
"Hu",
"Huawen",
""
],
[
"Yue",
"Chenxi",
""
],
[
"Zhang",
"Haiyang",
""
],
[
"Liu",
"Yiheng",
""
],
[
"Pan",
"Yi",
""
],
[
"Liu",
"Zhengliang",
""
],
[
"Sun",
"Lichao",
""
],
[
"Li",
"Xiang",
""
],
[
"Ge",
"Bao",
""
],
[
"Jiang",
"Xi",
""
],
[
"Zhu",
"Dajiang",
""
],
[
"Yuan",
"Yixuan",
""
],
[
"Shen",
"Dinggang",
""
],
[
"Liu",
"Tianming",
""
],
[
"Zhang",
"Shu",
""
]
] |
2304.14678 | Wen Zhang | Wen Zhang, Zhen Yao, Mingyang Chen, Zhiwei Huang and Huajun Chen | NeuralKG-ind: A Python Library for Inductive Knowledge Graph
Representation Learning | Accepted by SIGIR2023 Demonstration Track | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since the dynamic characteristics of knowledge graphs, many inductive
knowledge graph representation learning (KGRL) works have been proposed in
recent years, focusing on enabling prediction over new entities. NeuralKG-ind
is the first library of inductive KGRL as an important update of NeuralKG
library. It includes standardized processes, rich existing methods, decoupled
modules, and comprehensive evaluation metrics. With NeuralKG-ind, it is easy
for researchers and engineers to reproduce, redevelop, and compare inductive
KGRL methods. The library, experimental methodologies, and model
re-implementing results of NeuralKG-ind are all publicly released at
https://github.com/zjukg/NeuralKG/tree/ind .
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 08:09:08 GMT"
}
] | 1,682,899,200,000 | [
[
"Zhang",
"Wen",
""
],
[
"Yao",
"Zhen",
""
],
[
"Chen",
"Mingyang",
""
],
[
"Huang",
"Zhiwei",
""
],
[
"Chen",
"Huajun",
""
]
] |
2304.14712 | Eneko Osaba | Eneko Osaba, Esther Villar-Rodriguez and Sebasti\'an V. Romero | Benchmark dataset and instance generator for Real-World
Three-Dimensional Bin Packing Problems | 11 pages, 4 figures | Data in Brief, 109309 (2023) | 10.1016/j.dib.2023.109309 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this article, a benchmark for real-world bin packing problems is proposed.
This dataset consists of 12 instances of varying levels of complexity regarding
size (with the number of packages ranging from 38 to 53) and user-defined
requirements. In fact, several real-world-oriented restrictions were taken into
account to build these instances: i) item and bin dimensions, ii) weight
restrictions, iii) affinities among package categories iv) preferences for
package ordering and v) load balancing. Besides the data, we also offer an own
developed Python script for the dataset generation, coined Q4RealBPP-DataGen.
The benchmark was initially proposed to evaluate the performance of quantum
solvers. Therefore, the characteristics of this set of instances were designed
according to the current limitations of quantum devices. Additionally, the
dataset generator is included to allow the construction of general-purpose
benchmarks. The data introduced in this article provides a baseline that will
encourage quantum computing researchers to work on real-world bin packing
problems.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 09:29:43 GMT"
},
{
"version": "v2",
"created": "Tue, 9 May 2023 14:08:52 GMT"
},
{
"version": "v3",
"created": "Fri, 2 Jun 2023 08:11:15 GMT"
},
{
"version": "v4",
"created": "Thu, 29 Jun 2023 09:31:14 GMT"
}
] | 1,688,083,200,000 | [
[
"Osaba",
"Eneko",
""
],
[
"Villar-Rodriguez",
"Esther",
""
],
[
"Romero",
"Sebastián V.",
""
]
] |
2304.14742 | Caglar Demir | Caglar Demir, Michel Wiebesiek, Renzhong Lu, Axel-Cyrille Ngonga
Ngomo, Stefan Heindorf | LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric
Literals | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are
incomplete. Answering queries on such incomplete graphs is an important, but
challenging problem. Recently, a number of approaches, including complex query
decomposition (CQD), have been proposed to answer complex, multi-hop queries
with conjunctions and disjunctions on such graphs. However, all
state-of-the-art approaches only consider graphs consisting of entities and
relations, neglecting literal values. In this paper, we propose LitCQD -- an
approach to answer complex, multi-hop queries where both the query and the
knowledge graph can contain numeric literal values: LitCQD can answer queries
having numerical answers or having entity answers satisfying numerical
constraints. For example, it allows to query (1)~persons living in New York
having a certain age, and (2)~the average age of persons living in New York. We
evaluate LitCQD on query types with and without literal values. To evaluate
LitCQD, we generate complex, multi-hop queries and their expected answers on a
version of the FB15k-237 dataset that was extended by literal values.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 10:33:49 GMT"
}
] | 1,682,899,200,000 | [
[
"Demir",
"Caglar",
""
],
[
"Wiebesiek",
"Michel",
""
],
[
"Lu",
"Renzhong",
""
],
[
"Ngomo",
"Axel-Cyrille Ngonga",
""
],
[
"Heindorf",
"Stefan",
""
]
] |
2304.14778 | Arvid Becker | Arvid Becker, Pedro Cabalar, Mart\'in Di\'eguez, Torsten Schaub, Anna
Schuhmann | Metric Temporal Equilibrium Logic over Timed Traces | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In temporal extensions of Answer Set Programming (ASP) based on linear-time,
the behavior of dynamic systems is captured by sequences of states. While this
representation reflects their relative order, it abstracts away the specific
times associated with each state. However, timing constraints are important in
many applications like, for instance, when planning and scheduling go hand in
hand. We address this by developing a metric extension of linear-time temporal
equilibrium logic, in which temporal operators are constrained by intervals
over natural numbers. The resulting Metric Equilibrium Logic provides the
foundation of an ASP-based approach for specifying qualitative and quantitative
dynamic constraints. To this end, we define a translation of metric formulas
into monadic first-order formulas and give a correspondence between their
models in Metric Equilibrium Logic and Monadic Quantified Equilibrium Logic,
respectively. Interestingly, our translation provides a blue print for
implementation in terms of ASP modulo difference constraints.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 11:39:49 GMT"
},
{
"version": "v2",
"created": "Fri, 3 May 2024 12:40:35 GMT"
}
] | 1,714,953,600,000 | [
[
"Becker",
"Arvid",
""
],
[
"Cabalar",
"Pedro",
""
],
[
"Diéguez",
"Martín",
""
],
[
"Schaub",
"Torsten",
""
],
[
"Schuhmann",
"Anna",
""
]
] |
2304.14832 | Isabelle Kuhlmann | Isabelle Kuhlmann, Anna Gessler, Vivien Laszlo, Matthias Thimm | Comparison of SAT-based and ASP-based Algorithms for Inconsistency
Measurement | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present algorithms based on satisfiability problem (SAT) solving, as well
as answer set programming (ASP), for solving the problem of determining
inconsistency degrees in propositional knowledge bases. We consider six
different inconsistency measures whose respective decision problems lie on the
first level of the polynomial hierarchy. Namely, these are the contension
inconsistency measure, the forgetting-based inconsistency measure, the hitting
set inconsistency measure, the max-distance inconsistency measure, the
sum-distance inconsistency measure, and the hit-distance inconsistency measure.
In an extensive experimental analysis, we compare the SAT-based and ASP-based
approaches with each other, as well as with a set of naive baseline algorithms.
Our results demonstrate that overall, both the SAT-based and the ASP-based
approaches clearly outperform the naive baseline methods in terms of runtime.
The results further show that the proposed ASP-based approaches perform
superior to the SAT-based ones with regard to all six inconsistency measures
considered in this work. Moreover, we conduct additional experiments to explain
the aforementioned results in greater detail.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 13:18:55 GMT"
}
] | 1,682,899,200,000 | [
[
"Kuhlmann",
"Isabelle",
""
],
[
"Gessler",
"Anna",
""
],
[
"Laszlo",
"Vivien",
""
],
[
"Thimm",
"Matthias",
""
]
] |
2304.14918 | Johannes Czech | Johannes Czech, Jannis Bl\"uml, Kristian Kersting | Representation Matters: The Game of Chess Poses a Challenge to Vision
Transformers | 11 pages, 5 figures, 8 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | While transformers have gained the reputation as the "Swiss army knife of
AI", no one has challenged them to master the game of chess, one of the
classical AI benchmarks. Simply using vision transformers (ViTs) within
AlphaZero does not master the game of chess, mainly because ViTs are too slow.
Even making them more efficient using a combination of MobileNet and NextViT
does not beat what actually matters: a simple change of the input
representation and value loss, resulting in a greater boost of up to 180 Elo
points over AlphaZero.
| [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 15:33:39 GMT"
}
] | 1,682,899,200,000 | [
[
"Czech",
"Johannes",
""
],
[
"Blüml",
"Jannis",
""
],
[
"Kersting",
"Kristian",
""
]
] |
2305.00644 | Matthew Guzdial | Anurag Sarkar, Matthew Guzdial, Sam Snodgrass, Adam Summerville, Tiago
Machado and Gillian Smith | Procedural Content Generation via Knowledge Transformation (PCG-KT) | 15 pages, 14 figures | Sarkar, Anurag, et al. "Procedural Content Generation via
Knowledge Transformation (PCG-KT)." IEEE Transactions on Games (2023) | 10.1109/TG.2023.3270422 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the concept of Procedural Content Generation via Knowledge
Transformation (PCG-KT), a new lens and framework for characterizing PCG
methods and approaches in which content generation is enabled by the process of
knowledge transformation -- transforming knowledge derived from one domain in
order to apply it in another. Our work is motivated by a substantial number of
recent PCG works that focus on generating novel content via repurposing derived
knowledge. Such works have involved, for example, performing transfer learning
on models trained on one game's content to adapt to another game's content, as
well as recombining different generative distributions to blend the content of
two or more games. Such approaches arose in part due to limitations in PCG via
Machine Learning (PCGML) such as producing generative models for games lacking
training data and generating content for entirely new games. In this paper, we
categorize such approaches under this new lens of PCG-KT by offering a
definition and framework for describing such methods and surveying existing
works using this framework. Finally, we conclude by highlighting open problems
and directions for future research in this area.
| [
{
"version": "v1",
"created": "Mon, 1 May 2023 03:31:22 GMT"
}
] | 1,682,985,600,000 | [
[
"Sarkar",
"Anurag",
""
],
[
"Guzdial",
"Matthew",
""
],
[
"Snodgrass",
"Sam",
""
],
[
"Summerville",
"Adam",
""
],
[
"Machado",
"Tiago",
""
],
[
"Smith",
"Gillian",
""
]
] |
2305.00813 | Kaushik Roy | Amit Sheth, Kaushik Roy, Manas Gaur | Neurosymbolic AI -- Why, What, and How | To appear in IEEE Intelligent Systems | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Humans interact with the environment using a combination of perception -
transforming sensory inputs from their environment into symbols, and cognition
- mapping symbols to knowledge about the environment for supporting
abstraction, reasoning by analogy, and long-term planning. Human
perception-inspired machine perception, in the context of AI, refers to
large-scale pattern recognition from raw data using neural networks trained
using self-supervised learning objectives such as next-word prediction or
object recognition. On the other hand, machine cognition encompasses more
complex computations, such as using knowledge of the environment to guide
reasoning, analogy, and long-term planning. Humans can also control and explain
their cognitive functions. This seems to require the retention of symbolic
mappings from perception outputs to knowledge about their environment. For
example, humans can follow and explain the guidelines and safety constraints
driving their decision-making in safety-critical applications such as
healthcare, criminal justice, and autonomous driving. This article introduces
the rapidly emerging paradigm of Neurosymbolic AI combines neural networks and
knowledge-guided symbolic approaches to create more capable and flexible AI
systems. These systems have immense potential to advance both algorithm-level
(e.g., abstraction, analogy, reasoning) and application-level (e.g.,
explainable and safety-constrained decision-making) capabilities of AI systems.
| [
{
"version": "v1",
"created": "Mon, 1 May 2023 13:27:22 GMT"
}
] | 1,699,315,200,000 | [
[
"Sheth",
"Amit",
""
],
[
"Roy",
"Kaushik",
""
],
[
"Gaur",
"Manas",
""
]
] |
2305.02077 | Rushrukh Rayan | Rushrukh Rayan, Cogan Shimizu, Pascal Hitzler | An Ontology Design Pattern for Role-Dependent Names | 6 pages, 5 Figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an ontology design pattern for modeling Names as part of Roles, to
capture scenarios where an Agent performs different Roles using different Names
associated with the different Roles. Examples of an Agent performing a Role
using different Names are rather ubiquitous, e.g., authors who write under
different pseudonyms, or different legal names for citizens of more than one
country. The proposed pattern is a modified merger of a standard Agent Role and
a standard Name pattern stub.
| [
{
"version": "v1",
"created": "Wed, 3 May 2023 12:28:48 GMT"
}
] | 1,683,158,400,000 | [
[
"Rayan",
"Rushrukh",
""
],
[
"Shimizu",
"Cogan",
""
],
[
"Hitzler",
"Pascal",
""
]
] |
2305.04315 | Dustin Dannenhauer | Matthew Molineaux, Dustin Dannenhauer, Eric Kildebeck | A Framework for Characterizing Novel Environment Transformations in
General Environments | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | To be robust to surprising developments, an intelligent agent must be able to
respond to many different types of unexpected change in the world. To date,
there are no general frameworks for defining and characterizing the types of
environment changes that are possible. We introduce a formal and theoretical
framework for defining and categorizing environment transformations, changes to
the world an agent inhabits. We introduce two types of environment
transformation: R-transformations which modify environment dynamics and
T-transformations which modify the generation process that produces scenarios.
We present a new language for describing domains, scenario generators, and
transformations, called the Transformation and Simulator Abstraction Language
(T-SAL), and a logical formalism that rigorously defines these concepts. Then,
we offer the first formal and computational set of tests for eight categories
of environment transformations. This domain-independent framework paves the way
for describing unambiguous classes of novelty, constrained and
domain-independent random generation of environment transformations,
replication of environment transformation studies, and fair evaluation of agent
robustness.
| [
{
"version": "v1",
"created": "Sun, 7 May 2023 15:53:07 GMT"
}
] | 1,683,590,400,000 | [
[
"Molineaux",
"Matthew",
""
],
[
"Dannenhauer",
"Dustin",
""
],
[
"Kildebeck",
"Eric",
""
]
] |
2305.04357 | Fabio Massimo Zennaro | Fabio Massimo Zennaro, Paolo Turrini, Theodoros Damoulas | Quantifying Consistency and Information Loss for Causal Abstraction
Learning | 9 pages, 9 pages appendix, 2 figures, IJCAI 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Structural causal models provide a formalism to express causal relations
between variables of interest. Models and variables can represent a system at
different levels of abstraction, whereby relations may be coarsened and refined
according to the need of a modeller. However, switching between different
levels of abstraction requires evaluating a trade-off between the consistency
and the information loss among different models. In this paper we introduce a
family of interventional measures that an agent may use to evaluate such a
trade-off. We consider four measures suited for different tasks, analyze their
properties, and propose algorithms to evaluate and learn causal abstractions.
Finally, we illustrate the flexibility of our setup by empirically showing how
different measures and algorithmic choices may lead to different abstractions.
| [
{
"version": "v1",
"created": "Sun, 7 May 2023 19:10:28 GMT"
}
] | 1,683,590,400,000 | [
[
"Zennaro",
"Fabio Massimo",
""
],
[
"Turrini",
"Paolo",
""
],
[
"Damoulas",
"Theodoros",
""
]
] |
2305.05560 | Willem R\"opke | Willem R\"opke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann
Now\'e and Diederik M. Roijers | Distributional Multi-Objective Decision Making | Accepted at IJCAI 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For effective decision support in scenarios with conflicting objectives, sets
of potentially optimal solutions can be presented to the decision maker. We
explore both what policies these sets should contain and how such sets can be
computed efficiently. With this in mind, we take a distributional approach and
introduce a novel dominance criterion relating return distributions of policies
directly. Based on this criterion, we present the distributional undominated
set and show that it contains optimal policies otherwise ignored by the Pareto
front. In addition, we propose the convex distributional undominated set and
prove that it comprises all policies that maximise expected utility for
multivariate risk-averse decision makers. We propose a novel algorithm to learn
the distributional undominated set and further contribute pruning operators to
reduce the set to the convex distributional undominated set. Through
experiments, we demonstrate the feasibility and effectiveness of these methods,
making this a valuable new approach for decision support in real-world
problems.
| [
{
"version": "v1",
"created": "Tue, 9 May 2023 15:47:56 GMT"
},
{
"version": "v2",
"created": "Fri, 19 May 2023 08:12:57 GMT"
},
{
"version": "v3",
"created": "Tue, 18 Jul 2023 10:59:46 GMT"
}
] | 1,689,724,800,000 | [
[
"Röpke",
"Willem",
""
],
[
"Hayes",
"Conor F.",
""
],
[
"Mannion",
"Patrick",
""
],
[
"Howley",
"Enda",
""
],
[
"Nowé",
"Ann",
""
],
[
"Roijers",
"Diederik M.",
""
]
] |
2305.06951 | Viet-Man Le | Viet-Man Le, Cristian Vidal Silva, Alexander Felfernig, David
Benavides, Jos\'e Galindo, Thi Ngoc Trang Tran | FastDiagP: An Algorithm for Parallelized Direct Diagnosis | presented at The 37th AAAI Conference on Artificial Intelligence,
AAAI'23, Washington DC, USA | null | 10.1609/aaai.v37i5.25792 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Constraint-based applications attempt to identify a solution that meets all
defined user requirements. If the requirements are inconsistent with the
underlying constraint set, algorithms that compute diagnoses for inconsistent
constraints should be implemented to help users resolve the "no solution could
be found" dilemma. FastDiag is a typical direct diagnosis algorithm that
supports diagnosis calculation without predetermining conflicts. However, this
approach faces runtime performance issues, especially when analyzing complex
and large-scale knowledge bases. In this paper, we propose a novel algorithm,
so-called FastDiagP, which is based on the idea of speculative programming.
This algorithm extends FastDiag by integrating a parallelization mechanism that
anticipates and pre-calculates consistency checks requested by FastDiag. This
mechanism helps to provide consistency checks with fast answers and boosts the
algorithm's runtime performance. The performance improvements of our proposed
algorithm have been shown through empirical results using the Linux-2.6.3.33
configuration knowledge base.
| [
{
"version": "v1",
"created": "Thu, 11 May 2023 16:26:23 GMT"
}
] | 1,692,057,600,000 | [
[
"Le",
"Viet-Man",
""
],
[
"Silva",
"Cristian Vidal",
""
],
[
"Felfernig",
"Alexander",
""
],
[
"Benavides",
"David",
""
],
[
"Galindo",
"José",
""
],
[
"Tran",
"Thi Ngoc Trang",
""
]
] |
2305.07465 | Zhiyu Lin | Zhiyu Lin, Upol Ehsan, Rohan Agarwal, Samihan Dani, Vidushi Vashishth,
Mark Riedl | Beyond Prompts: Exploring the Design Space of Mixed-Initiative
Co-Creativity Systems | Accepted by ICCC'23 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generative Artificial Intelligence systems have been developed for image,
code, story, and game generation with the goal of facilitating human
creativity. Recent work on neural generative systems has emphasized one
particular means of interacting with AI systems: the user provides a
specification, usually in the form of prompts, and the AI system generates the
content. However, there are other configurations of human and AI coordination,
such as co-creativity (CC) in which both human and AI systems can contribute to
content creation, and mixed-initiative (MI) in which both human and AI systems
can initiate content changes. In this paper, we define a hypothetical human-AI
configuration design space consisting of different means for humans and AI
systems to communicate creative intent to each other. We conduct a human
participant study with 185 participants to understand how users want to
interact with differently configured MI-CC systems. We find out that MI-CC
systems with more extensive coverage of the design space are rated higher or on
par on a variety of creative and goal-completion metrics, demonstrating that
wider coverage of the design space can improve user experience and achievement
when using the system; Preference varies greatly between expertise groups,
suggesting the development of adaptive, personalized MI-CC systems;
Participants identified new design space dimensions including scrutability --
the ability to poke and prod at models -- and explainability.
| [
{
"version": "v1",
"created": "Wed, 3 May 2023 22:32:37 GMT"
}
] | 1,684,108,800,000 | [
[
"Lin",
"Zhiyu",
""
],
[
"Ehsan",
"Upol",
""
],
[
"Agarwal",
"Rohan",
""
],
[
"Dani",
"Samihan",
""
],
[
"Vashishth",
"Vidushi",
""
],
[
"Riedl",
"Mark",
""
]
] |
2305.07665 | Md Jalil Piran Prof. | Sitara Afzal, Haseeb Ali Khan, Imran Ullah Khan, Md. Jalil Piran, Jong
Weon Lee | A Comprehensive Survey on Affective Computing; Challenges, Trends,
Applications, and Future Directions | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | As the name suggests, affective computing aims to recognize human emotions,
sentiments, and feelings. There is a wide range of fields that study affective
computing, including languages, sociology, psychology, computer science, and
physiology. However, no research has ever been done to determine how machine
learning (ML) and mixed reality (XR) interact together. This paper discusses
the significance of affective computing, as well as its ideas, conceptions,
methods, and outcomes. By using approaches of ML and XR, we survey and discuss
recent methodologies in affective computing. We survey the state-of-the-art
approaches along with current affective data resources. Further, we discuss
various applications where affective computing has a significant impact, which
will aid future scholars in gaining a better understanding of its significance
and practical relevance.
| [
{
"version": "v1",
"created": "Mon, 8 May 2023 10:42:46 GMT"
}
] | 1,684,195,200,000 | [
[
"Afzal",
"Sitara",
""
],
[
"Khan",
"Haseeb Ali",
""
],
[
"Khan",
"Imran Ullah",
""
],
[
"Piran",
"Md. Jalil",
""
],
[
"Lee",
"Jong Weon",
""
]
] |
2305.07903 | Adam Pease | Chad Brown, Adam Pease, Josef Urban | Translating SUMO-K to Higher-Order Set Theory | 17 pages including references | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We describe a translation from a fragment of SUMO (SUMO-K) into higher-order
set theory. The translation provides a formal semantics for portions of SUMO
which are beyond first-order and which have previously only had an informal
interpretation. It also for the first time embeds a large common-sense ontology
into a very secure interactive theorem proving system. We further extend our
previous work in finding contradictions in SUMO from first order constructs to
include a portion of SUMO's higher order constructs. Finally, using the
translation, we can create problems that can be proven using higher-order
interactive and automated theorem provers. This is tested in several systems
and can be used to form a corpus of higher-order common-sense reasoning
problems.
| [
{
"version": "v1",
"created": "Sat, 13 May 2023 12:03:52 GMT"
}
] | 1,684,195,200,000 | [
[
"Brown",
"Chad",
""
],
[
"Pease",
"Adam",
""
],
[
"Urban",
"Josef",
""
]
] |
2305.08049 | Marcus Hoerger | Marcus Hoerger, Hanna Kurniawati, Dirk Kroese, Nan Ye | A Surprisingly Simple Continuous-Action POMDP Solver: Lazy Cross-Entropy
Search Over Policy Trees | To be published in the proceedings of The 38th Annual AAAI Conference
on Artificial Intelligence | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Partially Observable Markov Decision Process (POMDP) provides a
principled framework for decision making in stochastic partially observable
environments. However, computing good solutions for problems with continuous
action spaces remains challenging. To ease this challenge, we propose a simple
online POMDP solver, called Lazy Cross-Entropy Search Over Policy Trees
(LCEOPT). At each planning step, our method uses a novel lazy Cross-Entropy
method to search the space of policy trees, which provide a simple policy
representation. Specifically, we maintain a distribution on promising
finite-horizon policy trees. The distribution is iteratively updated by
sampling policies, evaluating them via Monte Carlo simulation, and refitting
them to the top-performing ones. Our method is lazy in the sense that it
exploits the policy tree representation to avoid redundant computations in
policy sampling, evaluation, and distribution update. This leads to
computational savings of up to two orders of magnitude. Our LCEOPT is
surprisingly simple as compared to existing state-of-the-art methods, yet
empirically outperforms them on several continuous-action POMDP problems,
particularly for problems with higher-dimensional action spaces.
| [
{
"version": "v1",
"created": "Sun, 14 May 2023 03:12:53 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Dec 2023 14:03:11 GMT"
}
] | 1,702,944,000,000 | [
[
"Hoerger",
"Marcus",
""
],
[
"Kurniawati",
"Hanna",
""
],
[
"Kroese",
"Dirk",
""
],
[
"Ye",
"Nan",
""
]
] |
2305.08116 | Arnaud Soulet | Lo\"ick Lhote, B\'eatrice Markhoff, Arnaud Soulet | The Structure and Dynamics of Knowledge Graphs, with Superficiality | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large knowledge graphs combine human knowledge garnered from projects ranging
from academia and institutions to enterprises and crowdsourcing. Within such
graphs, each relationship between two nodes represents a basic fact involving
these two entities. The diversity of the semantics of relationships constitutes
the richness of knowledge graphs, leading to the emergence of singular
topologies, sometimes chaotic in appearance. However, this complex
characteristic can be modeled in a simple way by introducing the concept of
superficiality, which controls the overlap between relationships whose facts
are generated independently. With this model, superficiality also regulates the
balance of the global distribution of knowledge by determining the proportion
of misdescribed entities. This is the first model for the structure and
dynamics of knowledge graphs. It leads to a better understanding of formal
knowledge acquisition and organization.
| [
{
"version": "v1",
"created": "Sun, 14 May 2023 10:16:07 GMT"
},
{
"version": "v2",
"created": "Tue, 16 May 2023 14:32:20 GMT"
},
{
"version": "v3",
"created": "Fri, 31 May 2024 16:32:44 GMT"
}
] | 1,717,372,800,000 | [
[
"Lhote",
"Loïck",
""
],
[
"Markhoff",
"Béatrice",
""
],
[
"Soulet",
"Arnaud",
""
]
] |
2305.08144 | Danyang Zhang | Danyang Zhang, Hongshen Xu, Zihan Zhao, Lu Chen, Ruisheng Cao, Kai Yu | Mobile-Env: An Evaluation Platform and Benchmark for LLM-GUI Interaction | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The User Interface (UI) is pivotal for human interaction with the digital
world, facilitating efficient control of machines, information navigation, and
complex task completion. To achieve easy, efficient, and free interactions,
researchers have been exploring the potential of encapsulating the traditional
Programming Language Interfaces (PLIs) and Graphical User Interfaces (GUIs)
into Natural Language Interfaces (NLIs). However, due to the limited
capabilities of small models, traditional work mainly focuses on tasks for
which only a single step is needed. This largely constrains the application of
NLIs. Recently, Large Language Models (LLMs) have exhibited robust reasoning
and planning abilities, yet their potential for multi-turn interactions in
complex environments remains under-explored. To assess LLMs as NLIs in
real-world graphical environments, we introduce the GUI interaction platform,
Mobile-Env, specifically on mobile apps. Mobile-Env enhances interaction
flexibility, task extensibility, and environment adaptability compared with
previous environments. A GUI task set based on WikiHow app is collected on
Mobile-Env to form a benchmark covering a range of GUI interaction
capabilities. We further conduct comprehensive evaluations of LLM agents,
including various versions of GPT, LLaMA 2, and AgentLM, on WikiHow task set to
acquire insights into the potentials and challenges of LLMs in GUI
interactions.
| [
{
"version": "v1",
"created": "Sun, 14 May 2023 12:31:03 GMT"
},
{
"version": "v2",
"created": "Wed, 14 Jun 2023 09:20:46 GMT"
},
{
"version": "v3",
"created": "Sat, 24 Feb 2024 12:43:14 GMT"
}
] | 1,708,992,000,000 | [
[
"Zhang",
"Danyang",
""
],
[
"Xu",
"Hongshen",
""
],
[
"Zhao",
"Zihan",
""
],
[
"Chen",
"Lu",
""
],
[
"Cao",
"Ruisheng",
""
],
[
"Yu",
"Kai",
""
]
] |
2305.08234 | Jakub Kowalski | Jakub Kowalski, Rados{\l}aw Miernik, Katarzyna Polak, Dominik Budzki,
Damian Kowalik | Introducing Tales of Tribute AI Competition | Extended version of IEEE Conference on Games 2024 paper | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presents a new AI challenge, the Tales of Tribute AI Competition
(TOTAIC), based on a two-player deck-building card game released with the High
Isle chapter of The Elder Scrolls Online. Currently, there is no other AI
competition covering Collectible Card Games (CCG) genre, and there has never
been one that targets a deck-building game. Thus, apart from usual CCG-related
obstacles to overcome, like randomness, hidden information, and large branching
factor, the successful approach additionally requires long-term planning and
versatility. The game can be tackled with multiple approaches, including
classic adversarial search, single-player planning, and Neural Networks-based
algorithms. This paper introduces the competition framework, describes the
rules of the game, and presents the results of a tournament between sample AI
agents.
| [
{
"version": "v1",
"created": "Sun, 14 May 2023 19:55:56 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Jul 2023 06:10:26 GMT"
},
{
"version": "v3",
"created": "Thu, 14 Mar 2024 12:49:29 GMT"
},
{
"version": "v4",
"created": "Sun, 19 May 2024 12:14:30 GMT"
}
] | 1,716,249,600,000 | [
[
"Kowalski",
"Jakub",
""
],
[
"Miernik",
"Radosław",
""
],
[
"Polak",
"Katarzyna",
""
],
[
"Budzki",
"Dominik",
""
],
[
"Kowalik",
"Damian",
""
]
] |
2305.08511 | Maurice Funk | Balder ten Cate, Maurice Funk, Jean Christoph Jung, Carsten Lutz | SAT-Based PAC Learning of Description Logic Concepts | 19 pages, Long version of paper accepted at IJCAI 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose bounded fitting as a scheme for learning description logic
concepts in the presence of ontologies. A main advantage is that the resulting
learning algorithms come with theoretical guarantees regarding their
generalization to unseen examples in the sense of PAC learning. We prove that,
in contrast, several other natural learning algorithms fail to provide such
guarantees. As a further contribution, we present the system SPELL which
efficiently implements bounded fitting for the description logic
$\mathcal{ELH}^r$ based on a SAT solver, and compare its performance to a
state-of-the-art learner.
| [
{
"version": "v1",
"created": "Mon, 15 May 2023 10:20:31 GMT"
}
] | 1,684,195,200,000 | [
[
"Cate",
"Balder ten",
""
],
[
"Funk",
"Maurice",
""
],
[
"Jung",
"Jean Christoph",
""
],
[
"Lutz",
"Carsten",
""
]
] |
2305.08664 | Zhaori Guo | Zhaori Guo, Timothy J. Norman, Enrico H. Gerding | MADDM: Multi-Advisor Dynamic Binary Decision-Making by Maximizing the
Utility | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Being able to infer ground truth from the responses of multiple imperfect
advisors is a problem of crucial importance in many decision-making
applications, such as lending, trading, investment, and crowd-sourcing. In
practice, however, gathering answers from a set of advisors has a cost.
Therefore, finding an advisor selection strategy that retrieves a reliable
answer and maximizes the overall utility is a challenging problem. To address
this problem, we propose a novel strategy for optimally selecting a set of
advisers in a sequential binary decision-making setting, where multiple
decisions need to be made over time. Crucially, we assume no access to ground
truth and no prior knowledge about the reliability of advisers. Specifically,
our approach considers how to simultaneously (1) select advisors by balancing
the advisors' costs and the value of making correct decisions, (2) learn the
trustworthiness of advisers dynamically without prior information by asking
multiple advisers, and (3) make optimal decisions without access to the ground
truth, improving this over time. We evaluate our algorithm through several
numerical experiments. The results show that our approach outperforms two other
methods that combine state-of-the-art models.
| [
{
"version": "v1",
"created": "Mon, 15 May 2023 14:13:47 GMT"
}
] | 1,684,195,200,000 | [
[
"Guo",
"Zhaori",
""
],
[
"Norman",
"Timothy J.",
""
],
[
"Gerding",
"Enrico H.",
""
]
] |
2305.09091 | Brendan Conway-Smith | Brendan Conway-Smith and Robert L. West | AAAI 2022 Fall Symposium: System-1 and System-2 realized within the
Common Model of Cognition | Full Publication In Proceedings of AAAI 2022 Fall Symposium: Thinking
Fast & Slow and Other Cognitive Theories in AI. Link:
https://ceur-ws.org/Vol-3332/ | In Proceedings of AAAI Fall Symposium: Thinking Fast & Slow and
Other Cognitive Theories in AI. Vol.3332 (2022) | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Attempts to import dual-system descriptions of System-1 and System-2 into AI
have been hindered by a lack of clarity over their distinction. We address this
and other issues by situating System-1 and System-2 within the Common Model of
Cognition. Results show that what are thought to be distinctive characteristics
of System-1 and 2 instead form a spectrum of cognitive properties. The Common
Model provides a comprehensive vision of the computational units involved in
System-1 and System-2, their underlying mechanisms, and the implications for
learning, metacognition, and emotion.
| [
{
"version": "v1",
"created": "Tue, 16 May 2023 01:28:06 GMT"
},
{
"version": "v2",
"created": "Thu, 25 May 2023 00:43:22 GMT"
}
] | 1,685,059,200,000 | [
[
"Conway-Smith",
"Brendan",
""
],
[
"West",
"Robert L.",
""
]
] |
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