Information theoretic approach to detect collusion in multi-agent games

Trevor Bonjour, Vaneet Aggarwal, Bharat Bhargava
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:223-232, 2022.

Abstract

Collusion in a competitive multi-agent game occurs when two or more agents co-operate covertly to the disadvantage of others. Most competitive multi-agent games do not allow players to share information and explicitly prohibit collusion. In this paper, we present a novel way of detecting collusion using a domain-independent information-theoretic approach. Specifically, we show that the use of mutual information between actions of the agents provides a good indication of collusive behavior. Our experiments show that our method can detect varying levels of collusion in repeated simultaneous games like iterated Rock Paper Scissors. We further extend the detection to partially observable sequential games like poker and show the effectiveness of our methodology.

Cite this Paper

BibTeX
@InProceedings{pmlr-v180-bonjour22a, title = {Information theoretic approach to detect collusion in multi-agent games}, author = {Bonjour, Trevor and Aggarwal, Vaneet and Bhargava, Bharat}, booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence}, pages = {223--232}, year = {2022}, editor = {Cussens, James and Zhang, Kun}, volume = {180}, series = {Proceedings of Machine Learning Research}, month = {01--05 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v180/bonjour22a/bonjour22a.pdf}, url = {https://proceedings.mlr.press/v180/bonjour22a.html}, abstract = {Collusion in a competitive multi-agent game occurs when two or more agents co-operate covertly to the disadvantage of others. Most competitive multi-agent games do not allow players to share information and explicitly prohibit collusion. In this paper, we present a novel way of detecting collusion using a domain-independent information-theoretic approach. Specifically, we show that the use of mutual information between actions of the agents provides a good indication of collusive behavior. Our experiments show that our method can detect varying levels of collusion in repeated simultaneous games like iterated Rock Paper Scissors. We further extend the detection to partially observable sequential games like poker and show the effectiveness of our methodology.} }
Endnote
%0 Conference Paper %T Information theoretic approach to detect collusion in multi-agent games %A Trevor Bonjour %A Vaneet Aggarwal %A Bharat Bhargava %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-bonjour22a %I PMLR %P 223--232 %U https://proceedings.mlr.press/v180/bonjour22a.html %V 180 %X Collusion in a competitive multi-agent game occurs when two or more agents co-operate covertly to the disadvantage of others. Most competitive multi-agent games do not allow players to share information and explicitly prohibit collusion. In this paper, we present a novel way of detecting collusion using a domain-independent information-theoretic approach. Specifically, we show that the use of mutual information between actions of the agents provides a good indication of collusive behavior. Our experiments show that our method can detect varying levels of collusion in repeated simultaneous games like iterated Rock Paper Scissors. We further extend the detection to partially observable sequential games like poker and show the effectiveness of our methodology.
APA
Bonjour, T., Aggarwal, V. & Bhargava, B.. (2022). Information theoretic approach to detect collusion in multi-agent games. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:223-232 Available from https://proceedings.mlr.press/v180/bonjour22a.html.

Related Material