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

  • Games
  • AI
  • Skat
  • OpponentModel
  • MonteCarlo
  • Search
  • Inference

• Graduation date

  Fall 2011

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3HD79

• 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.