Automating Collusion Detection in Sequential Games Parisa Mazrooei and Christopher Archibald and Michael Bowling Computing Science Department, University of Alberta Edmonton, Alberta, T6G 2E8, Canada fmazrooei,archibal,
[email protected] Abstract which exceeds the parameters” of normal activity (Watson 2012). Human intervention in these systems comes in the Collusion is the practice of two or more parties deliberately form of comparisons with human-specified models of sus- cooperating to the detriment of others. While such behavior pect behavior, and the systems can naturally only identify may be desirable in certain circumstances, in many it is con- sidered dishonest and unfair. If agents otherwise hold strictly collusive behavior matching the model. These models are to the established rules, though, collusion can be challenging typically domain specific, and new models are required for to police. In this paper, we introduce an automatic method any new domain addressed. Since any model of collusive be- for collusion detection in sequential games. We achieve this havior will be based on past examples of such behavior, new through a novel object, called a collusion table, that captures forms of collusion typically go undetected. As a commu- the effects of collusive behavior, i.e., advantage to the collud- nity’s size increases, detecting collusion places a huge bur- ing parties, without assuming any particular pattern of behav- den on human experts, in terms of responding to complaints, ior. We show the effectiveness of this method in the domain investigating false positives from an automated system, or of poker, a popular game where collusion is prohibited. continually updating the set of known collusive behaviors.