How AI evolved with game and implementation of modern AI in game

Research Article
Open access

How AI evolved with game and implementation of modern AI in game

Bohan Shen 1*
  • 1 Beijing Jiaotong University    
  • *corresponding author 20722015@bjtu.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/15/20230829
ACE Vol.15
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-021-9​
ISBN (Online): 978-1-83558-022-6

Abstract

For a long time, game was a relatively unrecognized area by academic community, which lacks detailed and sufficient discussion. But with the growth of game industry, game AI has become a heated topic in recent years. As an important and evolving application of AI, there is a need to better discuss the application and future improvement of game AI technologies. This paper introduced history and breakthrough of AI made in game area. And made discussion centered on current implementation of some popular approaches for game AI, followed by the possible future of these technologies. Some new implementations like procedural content generation were then covered to further discuss future implementations of AI in game area. All in all, hot spots and development prospects of this research topic were prospected to enlighten future development of game AI.

Keywords:

game AI, supervised learning, reinforcement learning, deep learning

Shen,B. (2023). How AI evolved with game and implementation of modern AI in game. Applied and Computational Engineering,15,167-173.
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References

[1]. Westera, W., Prada, R., Mascarenhas, S., Santos, P.A., Dias, J., Guimarães, M. and Georgiadis, K. 'Artificial intelligence moving serious gaming: Presenting reusable game AI components', 2020 Edu. Infor. Tech., 25(1), 351+.

[2]. Risi, S., & Preuss, M. From chess and atari to starcraft and beyond: How game ai is driving the world of ai. 2020 KI-Künst. Intell., 34, 7-17.

[3]. Silver, David, et al. Mastering the game of Go with deep neural networks and tree search. 2016 Nature 529.7587: 484-489.

[4]. Lu, Yunlong, and Wenxin Li. Techniques and Paradigms in Modern Game AI Systems. 2022 Algorithms 15.8: 282.

[5]. Tesauro, Gerald. Temporal difference learning and TD-Gammon. 1995 Commun. ACM 38.3: 58-68.

[6]. Campbell, Murray, A. Joseph Hoane Jr, and Feng-hsiung Hsu. Deep blue. 2002 Artif. Intell. 134.1-2: 57-83.

[7]. Mnih, Volodymyr, et al. Playing atari with deep reinforcement learning. 2013 arXiv preprint arXiv:1312.5602.

[8]. Berner, Christopher, et al. Dota 2 with large scale deep reinforcement learning. 2019 arXiv preprint arXiv:1912.06680.

[9]. Oh, Inseok, et al. Creating pro-level AI for a real-time fighting game using deep reinforcement learning. 2021 IEEE Trans. Games 14.2: 212-220.

[10]. Ye, Deheng, et al. Towards playing full moba games with deep reinforcement learning. 2020 Adv. Neur. Infor. Proce. Sys. 33: 621-632.

[11]. Gunawan, Leonardo Jose, et al. Analyzing AI and the Impact in Video Games. 2022 4th Inter.Conf. Cyber. Intell. Sys.,1-9.

[12]. Torrado, Ruben Rodriguez, et al. Deep reinforcement learning for general video game ai. 2018 IEEE Conf. Comput. Intell. Games, 1-11.

[13]. Barriga, Nicolas A. A short introduction to procedural content generation algorithms for videogames. 2019 Intern. J. Artif. Intell. Tools 28.02: 1930001.

[14]. Zhang, Yuzhong, Guixuan Zhang, and Xinyuan Huang. A Survey of Procedural Content Generation for Games. 2022 Inter. Conf. Cul.Orient. Sci. Tech., 1-10.

[15]. Liu, Jialin, et al. Deep learning for procedural content generation. 2021 Neu.l Comput. Appl. 33.1: 19-37.


Cite this article

Shen,B. (2023). How AI evolved with game and implementation of modern AI in game. Applied and Computational Engineering,15,167-173.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN:978-1-83558-021-9​(Print) / 978-1-83558-022-6(Online)
Editor:Marwan Omar, Roman Bauer, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.15
ISSN:2755-2721(Print) / 2755-273X(Online)

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References

[1]. Westera, W., Prada, R., Mascarenhas, S., Santos, P.A., Dias, J., Guimarães, M. and Georgiadis, K. 'Artificial intelligence moving serious gaming: Presenting reusable game AI components', 2020 Edu. Infor. Tech., 25(1), 351+.

[2]. Risi, S., & Preuss, M. From chess and atari to starcraft and beyond: How game ai is driving the world of ai. 2020 KI-Künst. Intell., 34, 7-17.

[3]. Silver, David, et al. Mastering the game of Go with deep neural networks and tree search. 2016 Nature 529.7587: 484-489.

[4]. Lu, Yunlong, and Wenxin Li. Techniques and Paradigms in Modern Game AI Systems. 2022 Algorithms 15.8: 282.

[5]. Tesauro, Gerald. Temporal difference learning and TD-Gammon. 1995 Commun. ACM 38.3: 58-68.

[6]. Campbell, Murray, A. Joseph Hoane Jr, and Feng-hsiung Hsu. Deep blue. 2002 Artif. Intell. 134.1-2: 57-83.

[7]. Mnih, Volodymyr, et al. Playing atari with deep reinforcement learning. 2013 arXiv preprint arXiv:1312.5602.

[8]. Berner, Christopher, et al. Dota 2 with large scale deep reinforcement learning. 2019 arXiv preprint arXiv:1912.06680.

[9]. Oh, Inseok, et al. Creating pro-level AI for a real-time fighting game using deep reinforcement learning. 2021 IEEE Trans. Games 14.2: 212-220.

[10]. Ye, Deheng, et al. Towards playing full moba games with deep reinforcement learning. 2020 Adv. Neur. Infor. Proce. Sys. 33: 621-632.

[11]. Gunawan, Leonardo Jose, et al. Analyzing AI and the Impact in Video Games. 2022 4th Inter.Conf. Cyber. Intell. Sys.,1-9.

[12]. Torrado, Ruben Rodriguez, et al. Deep reinforcement learning for general video game ai. 2018 IEEE Conf. Comput. Intell. Games, 1-11.

[13]. Barriga, Nicolas A. A short introduction to procedural content generation algorithms for videogames. 2019 Intern. J. Artif. Intell. Tools 28.02: 1930001.

[14]. Zhang, Yuzhong, Guixuan Zhang, and Xinyuan Huang. A Survey of Procedural Content Generation for Games. 2022 Inter. Conf. Cul.Orient. Sci. Tech., 1-10.

[15]. Liu, Jialin, et al. Deep learning for procedural content generation. 2021 Neu.l Comput. Appl. 33.1: 19-37.