Cognitive Insights and Stable Coalition Matching for Fostering Multi-Agent Cooperation
Keywords: Multi-Agent Cooperation, LLM, Theory of Mind
Abstract: Cognitive abilities, such as Theory of Mind (ToM), play a vital role in facilitating cooperation in human social interactions. However, Large Language Model (LLM) agents with higher ToM abilities do not necessarily exhibit better cooperative behavior compared to those with lower ToM abilities, highlighting the complexity of translating human cognitive processes to artificial intelligent agents. To address this challenge, we propose a novel matching coalition mechanism that leverages the strengths of agents with different ToM levels by explicitly considering belief alignment and specialized abilities when forming coalitions. Our proposed stable coalition formation algorithm seeks to find the team that maximizes the potential for cooperative trends and ensures long-term viability. By incorporating cognitive insights into the design of multi-agent systems, our work demonstrates the potential of leveraging ToM to create more sophisticated and human-like coordination strategies that foster cooperation and improve overall system performance.
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Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 6717
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