Porimon：一种基于长短时知识增强生成的LLM宝可梦对战智能体
Porimon: An LLM-Based Pokémon Battle Agent Enhanced by Long/Short-Term Knowledge Augmented Generation
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中文总结 AI 辅助
本文提出长短时知识增强生成机制及Porimon智能体，通过利用历史状态、经验摘要和外部API提升LLM在宝可梦对战中的对手感知规划性能，实验表明其显著优于基线。
中文摘要 AI 辅助
在本文中，我们以宝可梦对战为案例研究，探讨如何在不进行额外微调的情况下，提升基于LLM的智能体在需要对手感知规划的任务中的性能。我们提出了长短时知识增强生成（LSTKAG）机制，该机制使基于LLM的智能体能够利用当前任务的历史状态，并根据当前状态从相似的历史任务实例中检索经验摘要。基于LSTKAG，我们设计了Porimon，一种用于宝可梦对战的基于LLM的智能体结构。为了优化，我们引入了外部API以实现精确的伤害计算和更详细的游戏信息。我们进行了包含15,000场对战的锦标赛式评估实验，用于超参数优化、消融研究和性能评估。结果表明，经过超参数优化的基于Porimon的玩家显著优于基于PokéLLMon（先前研究中提出的基于LLM的智能体结构）的玩家以及基于规则的启发式玩家。此外，我们的消融研究表明，Porimon的变体在游戏信息检索方面优于未扩展的版本，这显示了该扩展的贡献。然而，当前实验结果关于长期KAG的贡献尚不确定。这些结果表明，引入外部资源、当前任务历史状态的信息以及相似历史任务实例的经验摘要，可以提升为需要对手感知规划的任务而设计的基于LLM的智能体的性能。
英文摘要
In this paper, we use Pokémon Battles as a case study to investigate how to improve the performance of LLM-based agents in tasks that require opponent-aware planning without additional fine-tuning. We propose Long/Short-Term Knowledge Augmented Generation (LSTKAG), a mechanism that enables LLM-based agents to leverage past states of the current task and retrieve experience summaries from similar previous task instances based on the current state. Based on LSTKAG, we design Porimon, an LLM-based agent structure for Pokémon Battles. For optimization, we introduce an external API for precise damage calculation and more detailed information about the game. We conduct tournament-like evaluation experiments comprising 15,000 battles for hyperparameter optimization, ablation studies, and performance evaluation. The results indicate that Porimon-based players with hyperparameter optimization significantly outperform players based on PokéLLMon, an LLM-based agent structure proposed in previous research, and the rule-based heuristic player. Furthermore, our ablation study shows that Porimon variants outperform the one without extension in game information retrieval, which shows the contribution of that extension. However, the current experiment results are inconclusive regarding the contribution of Long-Term KAG. These results suggest that introducing external resources, information from previous states of the current task, and experience summaries from similar previous task instances could elevate the performance of LLM-based agents designed for tasks requiring opponent-aware planning.
