Machine Learning for Adaptive Computer Game Opponents

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dc.contributor.authorMiles, Jonathan Daviden_NZ
dc.date.accessioned2009-01-05T15:23:11Z
dc.date.available2009-07-29T15:32:26Z
dc.date.issued2009en_NZ
dc.description.abstractThis thesis investigates the use of machine learning techniques in computer games to create a computer player that adapts to its opponent's game-play. This includes first confirming that machine learning algorithms can be integrated into a modern computer game without have a detrimental effect on game performance, then experimenting with different machine learning techniques to maximize the computer player's performance. Experiments use three machine learning techniques; static prediction models, continuous learning, and reinforcement learning. Static models show the highest initial performance but are not able to beat a simple opponent. Continuous learning is able to improve the performance achieved with static models but the rate of improvement drops over time and the computer player is still unable to beat the opponent. Reinforcement learning methods have the highest rate of improvement but the lowest initial performance. This limits the effectiveness of reinforcement learning because a large number of episodes are required before performance becomes sufficient to match the opponent.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationMiles, J. D. (2009). Machine Learning for Adaptive Computer Game Opponents (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2779en
dc.identifier.urihttps://hdl.handle.net/10289/2779
dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectMachine Learningen_NZ
dc.subjectComputer Gamesen_NZ
dc.subjectAdaptive Learningen_NZ
dc.subjectGame Opponenten_NZ
dc.subjectComputer Opponenten_NZ
dc.titleMachine Learning for Adaptive Computer Game Opponentsen_NZ
dc.typeThesisen_NZ
pubs.place-of-publicationHamilton, New Zealanden_NZ
thesis.degree.disciplineSchool of Computer Scienceen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (MSc)en_NZ
uow.date.accession2009-01-05T15:23:11Zen_NZ
uow.date.available2009-07-29T15:32:26Zen_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/public/adt-uow20090105.152311en_NZ
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