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General game AI with statistical forward planning algorithms
- Chinese Journal of Intelligent Science and Technology Vol. 1, Issue 3, Pages: 219-227(2019)
- 作者机构：
1. 伦敦玛丽女王大学，英国 伦敦 E1 4NS
2. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
3. 中国科学院大学人工智能学院，北京100049
4. 青岛智能产业技术研究院，山东 青岛 266109
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.201935
CLC： TP391-
Published Online：2019-09，
Published：20 September 2019
- 稿件说明：
移动端阅览
LUCAS Simon, Tianyu SHEN, Xiao WANG, et al. General game AI with statistical forward planning algorithms[J]. Chinese journal of intelligent science and technology, 2019, 1(3): 219-227.
LUCAS Simon, Tianyu SHEN, Xiao WANG, et al. General game AI with statistical forward planning algorithms[J]. Chinese journal of intelligent science and technology, 2019, 1(3): 219-227. DOI： 10.11959/j.issn.2096-6652.201935.
摘要
统计前向规划（statistical forward planning，SFP）算法使用仿真模型（也称为前向模型）自适应地搜索有效的动作序列，此类算法提供了一种简单通用的方法，为各种游戏提供快速自适应的 AI 控制。介绍了两种常用的 SFP 算法：蒙特x卡罗树搜索和滚动层进化，并证明了在没有任何事先训练的情况下，SPF 算法就可以在各种视频游戏中出色地运行。
Abstract
Statistical forward planning (SFP) algorithms use a simulation model (also called forward model) to adaptively search for effective sequences of actions.They offer a simple and general way to provide rapidly adaptive AI controllers for a variety of games.The two powerful SFP example algorithms:Monte Carlo tree search and rolling horizon evolution were introduced in this paper and key insights into their working principles were provided.It is demonstrated that the algorithms are able to play a variety of video games surprisingly well without the need for any prior training.
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references
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