Online Learning From Observation For Interactive Computer Games
| dc.contributor.author | Hartley, Thomas | |
| dc.contributor.author | Mehdi, Quasim | |
| dc.contributor.author | Gough, Norman | |
| dc.date.accessioned | 2008-08-05T11:19:16Z | |
| dc.date.available | 2008-08-05T11:19:16Z | |
| dc.date.issued | 2005 | |
| dc.identifier.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. | |
| dc.identifier.isbn | 0-9549016-1-6 | |
| dc.identifier.uri | http://hdl.handle.net/2436/34172 | |
| dc.description.abstract | 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. | |
| dc.language.iso | en | |
| dc.publisher | University of Wolverhampton, School of Computing and Information Technology | |
| dc.subject | Games based learning | |
| dc.subject | E-learning | |
| dc.subject | Interactive computer games | |
| dc.subject | Architecture | |
| dc.subject | Online action prediction | |
| dc.subject | Interactive simulations | |
| dc.title | Online Learning From Observation For Interactive Computer Games | |
| dc.title.alternative | GamesProceedings of CGAIMS’2005 | |
| dc.type | Conference contribution | |
| refterms.dateFOA | 2018-08-20T13:27:05Z | |
| html.description.abstract | 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. |
