Detecting a Colluding Subset in a Simple Two-Dimensional Game

Collusion is covert co-operation between the participants of a game. Detecting the colluding players can require discerning and understanding the player's motivation, which is often difficult task even for humans. In this paper we analyse experimental data from a simple two-dimensional game using synthetic players. We calculate information gains of the features in the data to show how well they indicate collusion. Then we examine J4.8 decision tree classifiers learned from the data and use them to detect the colluding subsets

Collusion detection in online bridge

Collusion is a major unsolved security problem in online bridge: by illicitly exchanging card informa- tion over the telephone, instant messenger or the like, cheaters can gain huge advantages over honest play- ers. It is very hard if not impossible to prevent collu- sion from happening. Instead, we motivate an AI-based detection approach and discuss its challenges. We chal- lenge the AI community to create automated methods for detecting collusive traces left in game records with an accuracy that can be achieved by human masters