A Neuro-Symbolic Approach to Strategy Synthesis for Strategic Logics

• URL: http://arxiv.org/abs/2606.17962v1
• Date: Tue, 16 Jun 2026 14:13:48 GMT
• Title: A Neuro-Symbolic Approach to Strategy Synthesis for Strategic Logics
• Abstract summary: We introduce a neuro-symbolic framework that integrates large language models into the model-checking pipeline for Multi-Agent Systems.<n>We instantiate the framework for bounded strategic reasoning in NatATL and introduce the first NatATL strategy-synthesis dataset.<n> Experiments with an open-weight Qwen3-32B model show that our certified pipeline achieves 92% accuracy on strategy-synthesis outcomes.
• Score: 6.277618174935789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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