Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2010
Abstract
This paper proposes self-organizing neural networks for modeling behavior of non-player characters (NPC) in first person shooting games. Specifically, two classes of self-organizing neural models, namely Self-Generating Neural Networks (SGNN) and Fusion Architecture for Learning and Cognition (FALCON) are used to learn non-player characters' behavior rules according to recorded patterns. Behavior learning abilities of these two models are investigated by learning specific sample Bots in the Unreal Tournament game in a supervised manner. Our empirical experiments demonstrate that both SGNN and FALCON are able to recognize important behavior patterns and learn the necessary knowledge to operate in the Unreal environment. Comparing with SGNN, FALCON is more effective in behavior learning, in terms of lower complexity and higher fighting competency
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, July 18-23
First Page
3649
Last Page
3656
Identifier
10.1109/IJCNN.2010.5596471
Publisher
IEEE
City or Country
New York
Citation
FENG, Shu and TAN, Ah-hwee.
Self-organizing neural networks for behavior modeling in games. (2010). Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, July 18-23. 3649-3656.
Available at: https://ink.library.smu.edu.sg/sis_research/6800
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://www.scopus.com/inward/record.url?eid=2-s2.0-79959430523&partnerID=MN8TOARS