The Iterated Prisoners' Dilemma: 20 Years on
The Iterated Prisoners' Dilemma: 20 Years on
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning against opponents with bounded memory
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Maintaining cooperation in noisy environments
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Strategy generation in multi-agent imperfect-information pursuit games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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This paper focuses on the Noisy Iterated Prisoner's Dilemma, a version of the Iterated Prisoner's Dilemma (IPD) in which there is a nonzero probability that a "cooperate" action will accidentally be changed into a "defect" action and vice versa. Tit-For-Tat and other strategies that do quite well in the ordinary (non-noisy) IPD can do quite badly in the Noisy IPD.This paper presents a technique called symbolic noise detection, for detecting whether anomalies in player's behavior are deliberate or accidental. The key idea is to construct a model of the other agent's behavior, and watch for any deviation from this model. If the other agent's next action is inconsistent with this model, the inconsistency can be due either to noise or to a genuine change in their behavior; and we can often distinguish between two cases by waiting to see whether this inconsistency persists in next few moves.We entered several different versions of our strategy in the 2005 Iterated Prisoner's Dilemma competition, in Category 2 (noisy environments). Out of the 165 contestants in this category, our programs consistently ranked among top ten. The best of our programs ranked third, and it was beaten only by two "master-slave strategy" programs that each had a large number of "slave" programs feeding points to them.