Document Type
Conference Proceeding
Publisher
IEEE
Faculty
Faculty of Computing, Health and Science
School
School of Computer and Information Science
RAS ID
5413
Abstract
Computational intelligence methods are well-suited for use in computer controlled opponents for video games. In many other applications of these methods, the aim is to simulate near-optimal intelligent behaviour. But in video games, the aim is to provide interesting opponents for human players, not optimal ones. In this study, we trained neural network-based computer controlled opponents to play like a human in a popular first-person shooter. We then had gamers play-test these opponents as well as a hand-coded opponent, and surveyed them to find out which opponents they enjoyed more. Our results show that the neural network-based opponents were clearly preferred.
DOI
10.1109/IJCNN.2008.4633818
Access Rights
free_to_read
Comments
This is an Author's Accepted Manuscript of: Soni, B. A., & Hingston, P. F. (2008). Bots Trained to Play Like a Human are More Fun. Proceedings of International Joint Conference on Neural Networks. (pp. 363-369). Hong Kong. IEEE. Available here
© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.