Abstract
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 human-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 insight for researchers in the field of AI in human-computer gaming.
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This work was supported by National Natural Science Foundation of China (No. 61906197).
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Qi-Yue Yin received the Ph. D. degree in pattern recognition and intelligent system from National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA), China in 2017. He is currently an associate professor at Center for Research on Intelligent System and Engineering, CASIA, China.
His research interests include machine learning, pattern recognition and artificial intelligence on games.
Jun Yang received the Ph.D. degree in control science and engineering from Tsinghua University, China in 2011. He is currently a lecturer with Department of Automation, Tsinghua University, China.
His research interests include multi-agent reinforcement learning and game theory.
Kai-Qi Huang received the Ph. D. degree in communication and information processing from Southeast University, China in 2004. He is currently a professor at Center for Research on Intelligent System and Engineering, CASIA, China.
His research interests include visual surveillance, image understanding, pattern recognition, human-computer gaming and biological based vision.
Mei-Jing Zhao received the Ph. D. degree in pattern recognition and intelligent system from Integrated Information System Research Center, CASIA, China in 2016. She is currently an associate professor at Center for Research on Intelligent System and Engineering, CASIA, China.
Her research interests include semantic information processing, knowledge representation and reasoning.
Wan-Cheng Ni received the Ph.D. degree in contemporary integrated manufacturing systems from Department of Automation, Tsinghua University, China in 2007. She is currently a professor at Center for Research on Intelligent System and Engineering, CASIA, China.
Her research interests include information processing and knowledge discovery, group intelligent decision-making platform and evaluation.
Bin Liang received the Ph.D. degree in precision instruments and mechanology from Tsinghua University, China in 1994. He is currently a professor with Department of Automation, Tsinghua University, China.
His research interests include artificial intelligence, anomaly detection, space robotics, and fault-tolerant control.
Yan Huang received the Ph.D. degree in pattern recognition and intelligent system from National Laboratory of Pattern Recognition (NLPR), CASIA, China in 2017. He is currently an associate professor at NLPR, CASIA, China.
His research interests include visual language understanding and video analysis.
Shu Wu received the Ph.D. degree in computer science from University of Sherbrooke, Canada in 2012. He is currently an associate professor at National Laboratory of Pattern Recognition, CASIA, China.
His research interests include data mining, network content analysis and security.
Liang Wang received the Ph. D. degree in pattern recognition and intelligent system from National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, China in 2004. He is currently a professor at NLPR, CASIA, China.
His research interests include computer vision, pattern recognition, machine learning and data mining.
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Yin, QY., Yang, J., Huang, KQ. et al. AI in Human-computer Gaming: Techniques, Challenges and Opportunities. Mach. Intell. Res. (2023). https://doi.org/10.1007/s11633-022-1384-6
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DOI: https://doi.org/10.1007/s11633-022-1384-6
Keywords
- Human-computer gaming
- AI
- intelligent decision making
- deep reinforcement learning
- self-play