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AI in Human-computer Gaming: Techniques, Challenges and Opportunities
Qi-Yue Yin1,2; Jun Yang3; Kai-Qi Huang1,2,4; Mei-Jing Zhao1; Wan-Cheng Ni1,2; Bin Liang3; Yan Huang1,2; Shu Wu1,2; Liang Wang1,2,4
Source PublicationMachine Intelligence Research

With the breakthrough of AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all over the world. As a recognized standard for testing artificial intelligence, various human-computer gaming AI systems (AIs) have been developed, such as Libratus, OpenAI Five, and AlphaStar, which beat professional human players. The rapid development of human-computer gaming AIs indicates a big step for decision-making intelligence, and it seems that current techniques can handle very complex human-computer games. So, one natural question arises: What are the possible challenges of current techniques in human-computer gaming and what are the future trends? To answer the above question, in this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, first-person shooting game AIs, and real-time strategy game AIs. Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional hu man-level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer games; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs. Finally, we hope that this brief review can provide an introduction for beginners and inspire in sight for researchers in the field of AI in human-computer gaming.

KeywordHuman-computer gaming, AI, intelligent decision making, deep reinforcement learning, self-play
Sub direction classification其他
planning direction of the national heavy laboratory其他
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Video parsing
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Collection学术期刊_Machine Intelligence Research
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
3.Department of Automation, Tsinghua University, Beijing 100084, China
4.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Qi-Yue Yin,Jun Yang,Kai-Qi Huang,et al. AI in Human-computer Gaming: Techniques, Challenges and Opportunities[J]. Machine Intelligence Research,2023,20(3):299-317.
APA Qi-Yue Yin.,Jun Yang.,Kai-Qi Huang.,Mei-Jing Zhao.,Wan-Cheng Ni.,...&Liang Wang.(2023).AI in Human-computer Gaming: Techniques, Challenges and Opportunities.Machine Intelligence Research,20(3),299-317.
MLA Qi-Yue Yin,et al."AI in Human-computer Gaming: Techniques, Challenges and Opportunities".Machine Intelligence Research 20.3(2023):299-317.
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