Hedonic games model cooperative games where agents desire to form coalitions, and only care about the composition of the coalitions of which they are members. Focusing on various classes of dichotomous hedonic games, where each agent either approves or disapproves a given coalition, we propose the random extension, where players have an independent participation probability. We initiate the research on the computational complexity of computing the probability that coalitions and partitions are optimal or stable. While some cases admit efficient algorithms (e.g., agents approve only few coalitions), they become computationally hard (#P-hard) in their complementary scenario. We then investigate the distribution of coalitions in perfect partitions and their performance in majority games, where an agent approves coalitions in which the agent is friends with the majority of its members. When friendships independently form with a constant probability, we prove that the number of coalitions of size 3 converges in distribution to a Poisson random variable.

Complexity of Probabilistic Inference in Random Dichotomous Hedonic Games


Multi-agent reinforcement learning (MARL) requires coordination by some means of interaction between agents to efficiently solve tasks. Interaction graphs allow reasoning about joint actions based on the local structure of interactions, but they disregard the potential impact of an agent’s action on its neighbors’ behaviors, which could rapidly alter in dynamic settings. In this paper, we thus present a novel perspective on opponent modeling in domains with only local interactions using (level-1) Graph Probabilistic Recursive Reasoning (GrPR2). Unlike previous work on recursive reasoning, each agent iteratively best-responds to other agents’ policies over all possible local interactions. Agents’ policies are approximated via a variational Bayes scheme for capturing their uncertainties, and we prove that an induced variant of Q-learning converges under self-play when there exists only one Nash equilibrium. In cooperative settings, we further devise a variational lower bound on the likelihood of each agent's optimality. Opposed to other models, optimizing the resulting objective prevents each agent from attaining an unrealistic modelling of others, and yields an exact tabular Q-iteration method that holds convergence guarantees. Then, we deepen the recursion to level-k via Cognitive Hierarchy GrPR2 (GrPR2-CH), which lets each level-k player best-respond to a mixture of strictly lower levels in the hierarchy. We prove that: (1) level-3 reasoning is the optimal hierarchical level, maximizing each agent's expected return; and (2) the weak spot of the classical CH models is that 0-level is uniformly distributed, as it may introduce policy bias. Finally, we propose a practical actor-critic scheme, and illustrate that GrPR2-CH outperforms strong MARL baselines in the particle environment.

Optimizing Multi-Agent Coordination via Hierarchical Graph Probabilistic Recursive Reasoning

In this work, we define the consensus-prevention problem, which examines the canonical swarm robotic consensus problem from an adversarial point of view: how (if at all) is it possible to lead a swarm into a disagreement, that is, prevent them from reaching an agreement. We focus on consensus-prevention in physically grounded tasks, concentrating on influencing the direction of movement of a flocking swarm and guaranteeing that the swarm will never converge to the same direction by the use of external, predefined agents, referred to as diverting agents.

Spatial Consensus-Prevention in Robotic Swarms

Posters & Demos I

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

**Session Chair:** Fernando P. Santos

Oral Session 2C2-2

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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

S5: Multiagent Learning 3

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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Saar Cohen

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SHORT BIO

Presentations

Complexity of Probabilistic Inference in Random Dichotomous Hedonic Games

Optimizing Multi-Agent Coordination via Hierarchical Graph Probabilistic Recursive Reasoning

Spatial Consensus-Prevention in Robotic Swarms

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