Search, Inference and Opponent Modelling in an Expert-Caliber Skat Player

  • Author / Creator
    Long, Jeffrey Richard
  • In this dissertation we discuss problems of search, inference and opponent modelling in imperfect information games in the context of creating a computer player for the popular german card game skat. In so doing, we demonstrate three major contributions to the field of artificial intelligence research in games. First, we present our skat player Kermit which, using a synthesis of different techniques, decisively defeats previously existing computer players and displays playing strength comparable to human experts. Second, we propose a framework for evaluating game-playing algorithms with known theoretical flaws and explaining the success of such methods in different classes of games. Finally, we enhance Kermit with a simple but effective opponent modelling component that allows it to adapt and improve its performance against players of differing playing strength in real time.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Buro, Michael (Computing Science)
  • Examining committee members and their departments
    • Nau, Dana (Computing Science)
    • Bowling, Michael (Computing Science)
    • Schaeffer, Jonathan (Computing Science)
    • Gannon, Terry (Mathematical Sciences)