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dc.contributor.authorHartley, Thomas
dc.contributor.authorMehdi, Qasim
dc.contributor.authorGough, Norman
dc.date.accessioned2008-07-10T13:55:16Z
dc.date.available2008-07-10T13:55:16Z
dc.date.issued2004
dc.identifier.citationIn: Mehdi, Q. and Gough, N. (Eds.), Proceedings of CGAIDE’2004. 5th Game-On International Conference on Computer Games: Artificial Intelligence, Design and Education, 8-10 November, 2004, Microsoft Academic Campus, Reading UK
dc.identifier.isbn0-9549016-0-6
dc.identifier.urihttp://hdl.handle.net/2436/31518
dc.description.abstractThis paper presents the outcomes of a research project into the field of artificial intelligence (AI) and computer game AI. The project considered the problem of applying AI techniques to computer games. Current commercial computer games tend to use complex scripts to control AI opponents. This can result in poor and predictable gameplay. The use of academic AI techniques is a possible solution to overcome these shortcomings. This paper describes the process of applying Markov decision processes (MDPs) using the value iteration algorithm to a 2D real time computer game. We also introduce a new stopping criterion for value iteration, which has been designed for use in computer games and we discuss results from experiments conducted on the MDPs AI engine. This paper also outlines conclusions about how successful MDPs are in relation to a real computer game AI engine and how useful they might be to computer games developers.
dc.language.isoen
dc.publisherUniversity of Wolverhampton, School of Computing and Information Technology
dc.subjectGames
dc.subjectGames based learning
dc.subjectLearning technology
dc.subjectE-learning
dc.subjectAI
dc.subjectMarkov decision processes
dc.subjectValue iteration
dc.subjectArtificial Intelligence
dc.subjectAI in computer games
dc.titleApplying Markov decision processes to 2D real time games
dc.title.alternativeProceedings of CGAIDE’2004
dc.typeConference contribution
refterms.dateFOA2018-08-20T13:50:55Z
html.description.abstractThis paper presents the outcomes of a research project into the field of artificial intelligence (AI) and computer game AI. The project considered the problem of applying AI techniques to computer games. Current commercial computer games tend to use complex scripts to control AI opponents. This can result in poor and predictable gameplay. The use of academic AI techniques is a possible solution to overcome these shortcomings. This paper describes the process of applying Markov decision processes (MDPs) using the value iteration algorithm to a 2D real time computer game. We also introduce a new stopping criterion for value iteration, which has been designed for use in computer games and we discuss results from experiments conducted on the MDPs AI engine. This paper also outlines conclusions about how successful MDPs are in relation to a real computer game AI engine and how useful they might be to computer games developers.


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