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Collusion Detection in Sequential Games

  • Author / Creator
    Mazrooei, Parisa
  • Collusion is the deliberate cooperation of two or more parties to the detriment of others. While this behaviour can be highly profitable for colluders (for example, in auctions and online games), it is considered illegal and unfair in many sequential decision-making domains and presents many challenging problems in these systems. In this thesis we present an automatic collusion detection method for extensive form games. This method uses a novel object, called a collusion table, that aims to capture the effects of collusive behaviour on the utility of players without committing to any particular pattern of behaviour. We also introduce a general method for developing collusive agents which was necessary to create a dataset of labelled colluding and normal agents. The effectiveness of our collusion detection method is demonstrated experimentally. Our results show that this method provides promising accuracy, detecting collusion by both strong and weak agents.

  • Subjects / Keywords
  • Graduation date
    2012-09
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3HM0Q
  • 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
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Michael Bowling (Computing Science)
  • Examining committee members and their departments
    • Michael Buro (Computing Science)
    • Robert Holte (Computing Science)