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Undirected graphical models are compact representations of joint probability distributions over random variables. To solve inference tasks of interest, graphical models of arbitrary topology can be trained using empirical risk minimization.…

Machine Learning · Computer Science 2020-10-23 Adarsh K. Jeewajee , Leslie P. Kaelbling

Evolution of agents' dynamics of multiagent systems under consensus protocol in the face of jamming attacks is discussed, where centralized parties are able to influence the control signals of the agents. In this paper we focus on a…

Systems and Control · Electrical Eng. & Systems 2023-03-14 Yurid Nugraha , Tomohisa Hayakawa , Hideaki Ishii , Ahmet Cetinkaya , Quanyan Zhu

If the influence diagram (ID) depicting a Bayesian game is common knowledge to its players then additional assumptions may allow the players to make use of its embodied irrelevance statements. They can then use these to discover a simpler…

Computer Science and Game Theory · Computer Science 2017-04-10 Peter A. Thwaites , Jim Q. Smith

An information-theoretic framework known as integrated information theory (IIT) has been introduced recently for the study of the emergence of consciousness in the brain [D. Balduzzi and G. Tononi, PLoS Comput. Biol. 4, e1000091 (2008)].…

Neurons and Cognition · Quantitative Biology 2011-07-11 Andre Nathan , Valmir C. Barbosa

The assumptions of necessary rationality and necessary knowledge of strategies, also known as perfect prediction, lead to at most one surviving outcome, immune to the knowledge that the players have of them. Solutions concepts implementing…

Computer Science and Game Theory · Computer Science 2019-05-23 Ghislain Fourny

Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks. However, they exhibit numerous limitations that prevent their broader adoption in many real-world…

Artificial Intelligence · Computer Science 2024-02-05 Debarun Bhattacharjya , Junkyu Lee , Don Joven Agravante , Balaji Ganesan , Radu Marinescu

This paper considers an infinitely repeated three-player Bayesian game with lack of information on two sides, in which an informed player plays two zero-sum games simultaneously at each stage against two uninformed players. This is a…

Theoretical Economics · Economics 2023-03-10 Lucas Pahl

There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains…

Artificial Intelligence · Computer Science 2024-11-04 Zhenwei Tang , Difan Jiao , Reid McIlroy-Young , Jon Kleinberg , Siddhartha Sen , Ashton Anderson

We formulate and study a class of two-player zero-sum stochastic dynamic games with partial and asymmetric information. Information asymmetry introduces fundamental challenges involving \emph{belief representation} and \emph{theory of mind}…

Optimization and Control · Mathematics 2026-03-20 Yuxiang Guan , Iman Shames , Tyler Summers

To what extent can an external observer observing an equilibrium action distribution in an incomplete information game infer the underlying information structure? We investigate this issue in a general linear-quadratic-Gaussian framework. A…

Theoretical Economics · Economics 2024-03-19 Masaki Miyashita

In many engineered systems, agents make decisions under incomplete information, creating opportunities for a planner to influence decentralized behavior through signaling. We study how such signaling can be designed in parallel-network,…

Computer Science and Game Theory · Computer Science 2026-04-14 Yuwei Hu , Bryce L. Ferguson

As artificial intelligence becomes increasingly intelligent---in some cases, achieving superhuman performance---there is growing potential for humans to learn from and collaborate with algorithms. However, the ways in which AI systems…

Artificial Intelligence · Computer Science 2020-07-15 Reid McIlroy-Young , Siddhartha Sen , Jon Kleinberg , Ashton Anderson

Large Language Models (LLMs) act as powerful reasoning engines but struggle with "symbol grounding" in embodied environments, particularly when information is asymmetrically distributed. We investigate the Privileged Information Bias (or…

Artificial Intelligence · Computer Science 2025-12-19 Shaun Baek , Sam Liu , Joseph Ukpong

As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information…

Databases · Computer Science 2012-08-29 Rohit Raghunathan , Sushovan De , Subbarao Kambhampati

The classical, complete-information two-player games assume that the problem data (in particular the payoff matrix) is known exactly by both players. In a now famous result, Nash has shown that any such game has an equilibrium in mixed…

Computer Science and Game Theory · Computer Science 2015-12-11 Nicolas Loizou

Integrated Information Theory (IIT) is a prominent theory of consciousness that has at its centre measures that quantify the extent to which a system generates more information than the sum of its parts. While several candidate measures of…

Neurons and Cognition · Quantitative Biology 2019-01-30 Pedro A. M. Mediano , Anil K. Seth , Adam B. Barrett

An agent, or a coalition of agents, faces an ethical dilemma between several statements if she is forced to make a conscious choice between which of these statements will be true. This paper proposes to capture ethical dilemmas as a…

Artificial Intelligence · Computer Science 2021-03-03 Pavel Naumov , Rui-Jie Yew

When observing the actions of others, humans make inferences about why they acted as they did, and what this implies about the world; humans also use the fact that their actions will be interpreted in this manner, allowing them to act…

Imitation is a basic updating mechanism for strategy evolution in structured populations, determining how individuals sample social information and translate it into behavioral changes. Higher-order networks, such as hypergraphs, generalize…

Physics and Society · Physics 2026-02-11 Bingxin Lin , Lei Zhou , Hao Fang

Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting sparsity of observations. Prior self-supervised meth- ods either impute before learning,…

Machine Learning · Computer Science 2026-02-18 Xiao Xiang , David Restrepo , Hyewon Jeong , Yugang Jia , Leo Anthony Celi