Fuzzy-CLAM: Understanding Agent Decisions in Multi-Agent Systems Through Fuzzy Policy Modeling

Understanding and modeling the policies of other agents in multi-agent systems (MAS) is crucial for enabling effective interaction in complex, dynamic environments. This paper introduces the Fuzzy-CLAM method, a novel approach in the agent policy modeling field that integrates fuzzy logic with contrastive learning-based agent modeling (CLAM). Fuzzy-CLAM provides a transparent and explainable decision-making process by applying fuzzy logic to map agents' decision logic into if-then rules. A structure learning mechanism is employed to dynamically adapt fuzzy rules to changes in the policy representation distribution, ensuring robustness and reducing redundancy. Experimental results demonstrate that Fuzzy-CLAM achieves a competitive level of learning performance while offering high explainability for agents’ decision-making processes. These insights further suggest potential avenues for refining and advancing agent policy modeling techniques in MAS.