A Neuro-Symbolic Approach to Strategy Synthesis for Strategic Logics

Abstract:Reasoning about what agents can achieve through strategic interaction is a core challenge in Multi-Agent Systems (MAS). Logics for strategic ability, such as ATL, provide rigorous methods, but their adoption is often hindered by the computational cost of strategy synthesis. We introduce a neuro-symbolic framework that integrates large language models (LLMs) into the model-checking pipeline for MAS. The LLM acts as a strategy-generation oracle, proposing candidate strategies that are then formally validated by a standard MAS model checker. This generate-and-certify architecture uses LLM guidance to navigate large combinatorial strategy spaces while preserving formal soundness: generated strategies are accepted only when certified by the verifier. We instantiate the framework for bounded strategic reasoning in NatATL and introduce the first NatATL strategy-synthesis dataset, consisting of 4211 instances. Experiments with an open-weight Qwen3-32B model show that our certified pipeline achieves 92\% accuracy on strategy-synthesis outcomes.

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)

arXiv:2606.17962 [cs.MA]

(or arXiv:2606.17962v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2606.17962 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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