用大模型自动把社交场景描述转成可执行的规则程序
Generative Agents for Multi-Agent Autoformalization of Interaction Scenarios
- 用大模型生成游戏规则，通过求解器保证语法正确
- 在110个场景中实现76.5%~77%语义正确率
- 适合想快速构建博弈模拟的研究者或开发者
多智能体仿真广泛用于探索自然与人工智能体间的交互，但开发通常依赖领域知识且耗时。本文提出生成式智能体多智能体自动形式化框架（GAMA），利用大语言模型（LLMs）增强的智能体，将自然语言描述的游戏理论场景自动形式化为可执行逻辑程序，定义游戏规则，并通过基于求解器的验证确保语法正确性。为保障运行时有效性，采用迭代锦标赛机制测试生成的规则与策略，并在有真实结果的情况下进行精确语义验证。在涵盖五个2×2同时行动博弈的110个自然语言描述上，使用Claude 3.5 Sonnet实现100%语法正确、76.5%语义正确；使用GPT-4o实现99.82%语法正确、77%语义正确。该框架在自动形式化智能体策略方面也表现出高语义准确性。
原文摘要 · Abstract (English)
Multi-agent simulations are versatile tools for exploring interactions among natural and artificial agents, but their development typically demands domain expertise and manual effort. This work introduces the Generative Agents for Multi-Agent Autoformalization (GAMA) framework, which automates the formalization of interaction scenarios in simulations using agents augmented with large language models (LLMs). To demonstrate the application of GAMA, we use natural language descriptions of game-theoretic scenarios representing social interactions, and we autoformalize them into executable logic programs defining game rules, with syntactic correctness enforced through a solver-based validation. To ensure runtime validity, an iterative, tournament-based procedure tests the generated rules and strategies, followed by exact semantic validation when ground truth outcomes are available. In experiments with 110 natural language descriptions across five 2x2 simultaneous-move games, GAMA achieves 100% syntactic and 76.5% semantic correctness with Claude 3.5 Sonnet, and 99.82% syntactic and 77% semantic correctness with GPT-4o. The framework also shows high semantic accuracy in autoformalizing agents' strategies.
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