用越南棋类游戏测试大模型多步规划与决策能力
Can LLMs Play Ô Ăn Quan Game? A Study of Multi-Step Planning and Decision Making
- 设计攻防不同风格的智能体角色，模拟多样化策略
- 在Ô Ăn Quan游戏中验证Llama系列模型的策略执行效果
- 揭示大模型在动态博弈中的推理短板与潜在优势
本文通过传统越南棋类游戏Ô Ăn Quan，考察大语言模型（LLMs）的多步规划与决策能力。该游戏包含一系列策略性棋子移动与吃子机制，为评估模型的战略思维提供了独特环境。我们构建了从激进到防守的不同智能体人格，并以Ô Ăn Quan作为测试平台，评估Llama-3.2-3B-Instruct、Llama-3.1-8B-Instruct及Llama-3.3-70B-Instruct等模型在不同策略下的表现。实验旨在理解这些模型如何进行战略决策、规划动作并应对动态游戏状态，结果将揭示其在推理与策略上的优劣势，深化对大模型通用能力的认知。
原文摘要 · Abstract (English)
In this paper, we explore the ability of large language models (LLMs) to plan and make decisions through the lens of the traditional Vietnamese board game, Ô Ăn Quan. This game, which involves a series of strategic token movements and captures, offers a unique environment for evaluating the decision-making and strategic capabilities of LLMs. Specifically, we develop various agent personas, ranging from aggressive to defensive, and employ the Ô Ăn Quan game as a testbed for assessing LLM performance across different strategies. Through experimentation with models like Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, and Llama-3.3-70B-Instruct, we aim to understand how these models execute strategic decision-making, plan moves, and manage dynamic game states. The results will offer insights into the strengths and weaknesses of LLMs in terms of reasoning and strategy, contributing to a deeper understanding of their general capabilities.
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