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A Neuro-Symbolic Approach to Strategy Synthesis for Strategic Logics

Paper introducing a neuro-symbolic framework for strategy synthesis in Multi-Agent Systems.

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A Neuro-Symbolic Approach to Strategy Synthesis for Strategic Logics

By Marco Aruta, Vadim Malvone, Aniello Murano, Domenico Parente, Luca RizzutiarXiv
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The authors propose a generate-and-certify architecture that integrates large language models into the model-checking pipeline. This approach uses LLM guidance to navigate large combinatorial strategy spaces while preserving formal soundness.

The paper instantiates this framework for bounded strategic reasoning in NatATL and introduces a new dataset.

Abstract

The authors propose a generate-and-certify architecture that integrates large language models into the model-checking pipeline. This approach uses LLM guidance to navigate large combinatorial strategy spaces while preserving formal soundness. The paper instantiates this framework for bounded strategic reasoning in NatATL and introduces a new dataset.

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neuro-symbolic approachstrategy synthesismulti-agent systemsmodel checkinglarge language modelsformal soundnessAI AgentsLarge Language ModelsSemantic InteroperabilityOntology & Taxonomy
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