GAMA: Generative Agents for Multi-Agent Autoformalization

• URL: http://arxiv.org/abs/2412.08805v2
• Date: Tue, 18 Feb 2025 12:06:39 GMT
• Title: GAMA: Generative Agents for Multi-Agent Autoformalization
• Authors: Agnieszka Mensfelt, Kostas Stathis, Vince Trencsenyi,
• Abstract summary: We present a framework that enables the autoformalization of interaction scenarios using agents augmented by large language models (LLMs) The agents translate natural language descriptions of interactions into executable logic programs that define the rules of each game. A tournament simulation then tests the functionality of the generated game rules and strategies.
• Score: 3.5083201638203154
• License:
• Abstract: Multi-agent simulations facilitate the exploration of interactions among both natural and artificial agents. However, modelling real-world scenarios and developing simulations often requires substantial expertise and effort. To streamline this process, we present a framework that enables the autoformalization of interaction scenarios using agents augmented by large language models (LLMs) utilising game-theoretic formalisms. The agents translate natural language descriptions of interactions into executable logic programs that define the rules of each game, ensuring syntactic correctness through validation by a solver. A tournament simulation then tests the functionality of the generated game rules and strategies. After the tournament, if a ground truth payoff matrix is available, an exact semantic validation is performed. We evaluate our approach on a diverse set of 110 natural language descriptions exemplifying five $2\times2$ simultaneous-move games, achieving 100% syntactic and 76.5% semantic correctness in the generated game rules for Claude 3.5 Sonnet, and 99.82% syntactic and 77% semantic correctness for GPT-4o. Additionally, we demonstrate high semantic correctness in autoformalizing gameplay strategies. Overall, the results highlight the potential of autoformalization to leverage LLMs in generating formal reasoning modules for decision-making agents.

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