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arXiv 1111.37352011-11-16cs.LGcs.AI

A Bayesian Model for Plan Recognition in RTS Games applied to StarCraft

LPPA；Collège de France（法兰西公学院）；CNRS（法国国家科学研究中心）

Gabriel Synnaeve, Pierre Bessière

备注：: 7 pages; Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2011), Palo Alto : États-Unis (2011)

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The task of keyhole (unobtrusive) plan recognition is central to adaptive game AI. "Tech trees" or "build trees" are the core of real-time strategy (RTS) game strategic (long term) planning. This paper presents a generic and simple Bayesian model for RTS build tree prediction from noisy observations, which parameters are learned from replays (game logs). This unsupervised machine learning approach involves minimal work for the game developers as it leverage players' data (com- mon in RTS). We applied it to StarCraft1 and showed that it yields high quality and robust predictions, that can feed an adaptive AI.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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