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dc.contributor.authorHartley, Thomas
dc.contributor.authorMehdi, Qasim
dc.contributor.authorGough, Norman
dc.date.accessioned2008-08-05T11:19:16Z
dc.date.available2008-08-05T11:19:16Z
dc.date.issued2005
dc.identifier.citationIn: 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.isbn0-9549016-1-6
dc.identifier.urihttp://hdl.handle.net/2436/34172
dc.description.abstractThe 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.isoen
dc.publisherUniversity of Wolverhampton, School of Computing and Information Technology
dc.subjectGames based learning
dc.subjectE-learning
dc.subjectInteractive computer games
dc.subjectArchitecture
dc.subjectOnline action prediction
dc.subjectInteractive simulations
dc.titleOnline Learning From Observation For Interactive Computer Games
dc.title.alternativeGamesProceedings of CGAIMS’2005
dc.typeConference contribution
refterms.dateFOA2018-08-20T13:27:05Z
html.description.abstractThe 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.


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