用大模型实时指导星际争霸玩家决策，提升新手和残障玩家的游戏表现。
Adaptive Command: Real-Time Policy Adjustment via Language Models in StarCraft II
- 通过大模型+行为树架构，实现自然语言驱动的实时策略调整。
- 用户测试显示新手与残障玩家的决策质量与应变能力显著提升。
- 适合需要人机协同决策的复杂动态场景，如游戏、指挥系统等。
我们提出 Adaptive Command，一种将大型语言模型（LLMs）与行为树结合的新框架，用于在《星际争霸II》中实现实时战略决策。该系统通过自然语言交互增强人机协作，包含：(1) 基于大模型的战略顾问，(2) 用于动作执行的行为树，(3) 支持语音的自然语言接口。用户研究表明，该系统显著提升了玩家的决策质量与战略适应性，尤其对新手玩家及残障用户有明显帮助。本工作推动了实时人机协同决策的发展，其思路可扩展至各类复杂决策场景。
原文摘要 · Abstract (English)
We present Adaptive Command, a novel framework integrating large language models (LLMs) with behavior trees for real-time strategic decision-making in StarCraft II. Our system focuses on enhancing human-AI collaboration in complex, dynamic environments through natural language interactions. The framework comprises: (1) an LLM-based strategic advisor, (2) a behavior tree for action execution, and (3) a natural language interface with speech capabilities. User studies demonstrate significant improvements in player decision-making and strategic adaptability, particularly benefiting novice players and those with disabilities. This work contributes to the field of real-time human-AI collaborative decision-making, offering insights applicable beyond RTS games to various complex decision-making scenarios.
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