Chinese Journal of Intelligent Science and Technology ›› 2019, Vol. 1 ›› Issue (3): 219-227.doi: 10.11959/j.issn.2096-6652.201935

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General game AI with statistical forward planning algorithms

LUCAS Simon1,Tianyu SHEN2,3,Xiao WANG2,4(),Jie ZHANG2,4   

  1. 1 Queen Mary University of London,London E1 4NS,UK
    2 The State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
    3 School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China
    4 Qingdao Academy of Intelligent Industries,Qingdao 266109,China
  • Revised:2019-08-16 Online:2019-09-20 Published:2019-12-17
  • Supported by:
    The Young Elite Scientists Sponsorship Program of China Association of Science and Technology(2017QNRC001);The National Natural Science Foundation of China(61702519);The National Natural Science Foundation of China(71402178)


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.

Key words: statistical forward planning, game AI, rolling horizon evolution

CLC Number: 

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