A Neuro-Symbolic Approach to Strategy Synthesis for Strategic Logics
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 st...
| Authors | Marco Aruta, Vadim Malvone, Aniello Murano et al. |
|---|---|
| Primary category | cs.MA |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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