Theory of Mind Using Active Inference: A Framework for Multi-Agent Cooperation
- URL: http://arxiv.org/abs/2508.00401v1
- Date: Fri, 01 Aug 2025 08:02:35 GMT
- Title: Theory of Mind Using Active Inference: A Framework for Multi-Agent Cooperation
- Authors: Riddhi J. Pitliya, Ozan Catal, Toon Van de Maele, Corrado Pezzato, Tim Verbelen,
- Abstract summary: We present a novel approach to multi-agent cooperation by implementing theory of mind (ToM) within active inference.<n>ToM enables agents to reason about others' beliefs while planning their own actions.<n>We extend the sophisticated inference tree-based planning algorithm to systematically explore joint policy spaces.
- Score: 4.06613683722116
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a novel approach to multi-agent cooperation by implementing theory of mind (ToM) within active inference. ToM - the ability to understand that others can have differing knowledge and goals - enables agents to reason about others' beliefs while planning their own actions. Unlike previous active inference approaches to multi-agent cooperation, our method neither relies on task-specific shared generative models nor requires explicit communication, while being generalisable. In our framework, the ToM-equipped agent maintains distinct representations of its own and others' beliefs and goals. We extend the sophisticated inference tree-based planning algorithm to systematically explore joint policy spaces through recursive reasoning. Our approach is evaluated through collision avoidance and foraging task simulations. Results demonstrate that ToM-equipped agents cooperate better compared to non-ToM counterparts by being able to avoid collisions and reduce redundant efforts. Crucially, ToM agents accomplish this by inferring others' beliefs solely from observable behaviour. This work advances practical applications in artificial intelligence while providing computational insights into ToM.
Related papers
- ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [53.817538122688944]
We introduce Reinforced Meta-thinking Agents (ReMA) to elicit meta-thinking behaviors from Reasoning of Large Language Models (LLMs)<n>ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions.<n> Empirical results from single-turn experiments demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
arXiv Detail & Related papers (2025-03-12T16:05:31Z) - Large Language Models as Theory of Mind Aware Generative Agents with Counterfactual Reflection [31.38516078163367]
ToM-agent is designed to empower LLMs-based generative agents to simulate ToM in open-domain conversational interactions.<n>ToM-agent disentangles the confidence from mental states, facilitating the emulation of an agent's perception of its counterpart's mental states.<n>Our findings indicate that the ToM-agent can grasp the underlying reasons for their counterpart's behaviors beyond mere semantic-emotional supporting or decision-making based on common sense.
arXiv Detail & Related papers (2025-01-26T00:32:38Z) - Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task [56.92961847155029]
Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others.
Mutual Theory of Mind (MToM) arises when AI agents with ToM capability collaborate with humans.
We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent.
arXiv Detail & Related papers (2024-09-13T13:19:48Z) - Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement
Learning [5.314466196448188]
We present a method of grounding semantically meaningful, human-interpretable beliefs within policies modeled by deep networks.
We propose that ability of each agent to predict the beliefs of the other agents can be used as an intrinsic reward signal for multi-agent reinforcement learning.
arXiv Detail & Related papers (2023-07-03T17:07:18Z) - Learning Reward Machines in Cooperative Multi-Agent Tasks [75.79805204646428]
This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL)
It combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks.
The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments.
arXiv Detail & Related papers (2023-03-24T15:12:28Z) - Recursive Reasoning Graph for Multi-Agent Reinforcement Learning [44.890087638530524]
Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other.
Existing algorithms can suffer from an inability to accurately anticipate the influence of self-actions on other agents.
The proposed algorithm, referred to as the Recursive Reasoning Graph (R2G), shows state-of-the-art performance on multiple multi-agent particle and robotics games.
arXiv Detail & Related papers (2022-03-06T00:57:50Z) - ToM2C: Target-oriented Multi-agent Communication and Cooperation with
Theory of Mind [18.85252946546942]
Theory of Mind (ToM) builds socially intelligent agents who are able to communicate and cooperate effectively.
We demonstrate the idea in two typical target-oriented multi-agent tasks: cooperative navigation and multi-sensor target coverage.
arXiv Detail & Related papers (2021-10-15T18:29:55Z) - Emergence of Theory of Mind Collaboration in Multiagent Systems [65.97255691640561]
We propose an adaptive training algorithm to develop effective collaboration between agents with ToM.
We evaluate our algorithms with two games, where our algorithm surpasses all previous decentralized execution algorithms without modeling ToM.
arXiv Detail & Related papers (2021-09-30T23:28:00Z) - Deep Interactive Bayesian Reinforcement Learning via Meta-Learning [63.96201773395921]
The optimal adaptive behaviour under uncertainty over the other agents' strategies can be computed using the Interactive Bayesian Reinforcement Learning framework.
We propose to meta-learn approximate belief inference and Bayes-optimal behaviour for a given prior.
We show empirically that our approach outperforms existing methods that use a model-free approach, sample from the approximate posterior, maintain memory-free models of others, or do not fully utilise the known structure of the environment.
arXiv Detail & Related papers (2021-01-11T13:25:13Z) - Emergence of Pragmatics from Referential Game between Theory of Mind
Agents [64.25696237463397]
We propose an algorithm, using which agents can spontaneously learn the ability to "read between lines" without any explicit hand-designed rules.
We integrate the theory of mind (ToM) in a cooperative multi-agent pedagogical situation and propose an adaptive reinforcement learning (RL) algorithm to develop a communication protocol.
arXiv Detail & Related papers (2020-01-21T19:37:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.