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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2021
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2111.07631 |
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| _version_ | 1866917624928010240 |
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| author | Yin, Qiyue Yang, Jun Huang, Kaiqi Zhao, Meijing Ni, Wancheng Liang, Bin Huang, Yan Wu, Shu Wang, Liang |
| author_facet | Yin, Qiyue Yang, Jun Huang, Kaiqi Zhao, Meijing Ni, Wancheng Liang, Bin Huang, Yan Wu, Shu Wang, Liang |
| contents | With breakthrough of the AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all around the world. As a recognized standard for testing artificial intelligence, various human-computer gaming AI systems (AIs) have been developed such as the Libratus, OpenAI Five and AlphaStar, beating professional human players. The rapid development of human-computer gaming AIs indicate a big step of decision making intelligence, and it seems that current techniques can handle very complex human-computer games. So, one natural question raises: 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 gaming; 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 this brief review can provide an introduction for beginners, and inspire insights for researchers in the field of AI in human-computer gaming. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_07631 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | AI in Human-computer Gaming: Techniques, Challenges and Opportunities Yin, Qiyue Yang, Jun Huang, Kaiqi Zhao, Meijing Ni, Wancheng Liang, Bin Huang, Yan Wu, Shu Wang, Liang Artificial Intelligence With breakthrough of the AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all around the world. As a recognized standard for testing artificial intelligence, various human-computer gaming AI systems (AIs) have been developed such as the Libratus, OpenAI Five and AlphaStar, beating professional human players. The rapid development of human-computer gaming AIs indicate a big step of decision making intelligence, and it seems that current techniques can handle very complex human-computer games. So, one natural question raises: 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 gaming; 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 this brief review can provide an introduction for beginners, and inspire insights for researchers in the field of AI in human-computer gaming. |
| title | AI in Human-computer Gaming: Techniques, Challenges and Opportunities |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2111.07631 |