Online Learning From Observation For Interactive Computer Games

The research presented in this paper describes an architecture, which enables an agent to predict an observed entity’s actions (most likely a human’s) online. Case-based approaches have been utilised by a number of researchers for online action prediction in interactive applications. Our architecture builds on these works and provides a number of novel contributions. Specifically our architecture offers a more comprehensive state representation, behaviour prediction and a more robust case maintenance approach. The proposed architecture is fully described in terms of interactive simulations (specifically first person shooter (FPS) computer games); however it would be applicable to other interactive applications, such as intelligent tutoring and surveillance systems. We conclude the paper by evaluating our proposed architecture and discussing how the system will be implemented.

Citation

In: Mehdi, Q., Gough, N. and Elmaghraby, A. (Eds), Proceedings of CGAIMS’2005. 6th International Conference on Computer Games: Artificial Intelligence and Mobile Systems, 27-30 July 2005, Louisville, Kentucky, USA.

Publisher

University of Wolverhampton, School of Computing and Information Technology

URI

http://hdl.handle.net/2436/34172

Type

Conference contribution

Language

en

ISBN

0-9549016-1-6

Collections

Faculty of Science and Engineering