Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

Computer and Information Science

RAS ID

5413

Comments

This article was originally published as: 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. Original article 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.

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

 
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Link to publisher version (DOI)

10.1109/IJCNN.2008.4633818