大模型能当宝可梦对战选手，还能自动生成新内容。
Large Language Models as Pokémon Battle Agents: Strategic Play and Content Generation
- 让大模型根据战斗状态自主选招，不依赖预设逻辑。
- 在多模型测试中实现较高胜率与决策效率。
- 适合游戏设计、动态难度调节等交互娱乐场景。
宝可梦对战中的战略决策为评估大语言模型提供了独特场景。对战需分析属性相克、数值权衡与风险判断，这些能力类似人类策略思维。本文探究大语言模型能否作为合格的对战智能体，既能做出战术合理决策，又能生成新颖且平衡的游戏内容。我们构建了一个回合制宝可梦对战系统，使大模型基于当前战斗状态选择技能，而非预设逻辑。该框架包含核心机制：属性克制倍率、基于属性的伤害计算及多宝可梦队伍管理。通过多模型架构的系统评估，测量了胜率、决策延迟、属性匹配准确率与令牌效率。结果表明，大模型可在无需领域训练的情况下充当动态对手，是回合制策略游戏的一种实用替代方案。其兼具战术推理与内容生成的能力，使其可同时担任玩家与设计师，对程序化生成与自适应难度系统具有重要意义。
原文摘要 · Abstract (English)
Strategic decision-making in Pokémon battles presents a unique testbed for evaluating large language models. Pokémon battles demand reasoning about type matchups, statistical trade-offs, and risk assessment, skills that mirror human strategic thinking. This work examines whether Large Language Models (LLMs) can serve as competent battle agents, capable of both making tactically sound decisions and generating novel, balanced game content. We developed a turn-based Pokémon battle system where LLMs select moves based on battle state rather than pre-programmed logic. The framework captures essential Pokémon mechanics: type effectiveness multipliers, stat-based damage calculations, and multi-Pokémon team management. Through systematic evaluation across multiple model architectures we measured win rates, decision latency, type-alignment accuracy, and token efficiency. These results suggest LLMs can function as dynamic game opponents without domain-specific training, offering a practical alternative to reinforcement learning for turn-based strategic games. The dual capability of tactical reasoning and content creation, positions LLMs as both players and designers, with implications for procedural generation and adaptive difficulty systems in interactive entertainment.
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