Download pdf
A review of artificial intelligence in video games: From preset scripts to self-learning
- 1 NingBo BinHai International Cooperative School
* Author to whom correspondence should be addressed.
Abstract
It is now the 21st century, with the progressive development of various science and technology, such as artificial intelligence, big data, and so on, and these ever-evolving technologies have also greatly contributed to the development of today's flourishing video game field. This paper focuses on the development of artificial intelligence applications in video games over the past two decades, from preset scripts to self-learning processes, and adopts the research method of literature review. The paper concludes that the shift from pre-scripted to self-learning AI marks a shift in video games from experiences with clear rules and controlled processes to complex, dynamic, personalized experiences. This shift brings not only new opportunities but also new challenges. In the future, we can expect to see more research and practice to explore and take advantage of more of the possibilities of self-learning AI in video games.
Keywords
Artificial Intelligence, Video Games, Deep Learning, Prescriptive Scripting, Self-Learning
[1]. Rollings, A., & Adams, E. (2003). Andrew Rollings and Ernest Adams on game design. New Riders. https://api.semanticscholar.org.
[2]. Game Design: Theory and Practice by Rouse III, Richard(2004). Electronic Industry Press. 456.
[3]. A Formal Approach to Game Design and Game Research, Zubek(2004). TY- JOUR
[4]. Hunicke, Robin,Leblanc, Marc,Zubek, Robert,(2004). MDA: A Formal Approach to Game Design and Game Research,Workshop - Technical Report.
[5]. Laird, J. E., & van Lent, M. (2000). Human-level AI’s killer application: Interactive computer games. AI magazine, 21(2), 15-15.
[6]. Yannakakis, G. N., & Togelius, J. (2018). Artificial intelligence and games. Springer.
[7]. Mateas & Stern,(2005). Procedural Authorship: A Case-Study Of the Interactive Drama Façade.
[8]. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., … & Petersen, S. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
[9]. Berner, C., Brockman, G., Chan, B., Cheung, V., Debiak, P., Dennison, C.& Klimov, O. (2019). Dota 2 with Large Scale Deep Reinforcement Learning. arXiv preprint arXiv:1912.06680.
[10]. Justesen, N., Torrado, R. R., Bontrager, P., Khalifa, A., Togelius, J., & Risi, S. (2019). Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation. arXiv preprint arXiv:1806.10729.
[11]. Zook, A., Harrison, B., & Riedl, M. O. (2019). Monte-Carlo Tree Search for Simulation-Based Strategy Analysis. In FDG.
[12]. Laird, J. E., & Duchi, J. C. (2000). Creating human-like synthetic characters with multiple skill levels: A case study using the Soar quakebot. In AAAI/IAAI (Vol. 2000, pp. 403-408).
[13]. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
Cite this article
Zhu,J. (2024). A review of artificial intelligence in video games: From preset scripts to self-learning. Applied and Computational Engineering,49,149-153.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).