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      • University of Waikato Theses
      • Masters Degree Theses
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      Machine Learning for Adaptive Computer Game Opponents

      Miles, Jonathan David
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      Miles, 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/10289/2779
      Permanent Research Commons link: https://hdl.handle.net/10289/2779
      Abstract
      This 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.
      Date
      2009
      Type
      Thesis
      Degree Name
      Master of Science (MSc)
      Publisher
      The University of Waikato
      Rights
      All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
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      • Masters Degree Theses [2387]
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