Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Synthesis of UNIX Programs Using Derivational Analogy
Machine Learning
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Theoretical Results on Reinforcement Learning with Temporally Abstract Options
ECML '98 Proceedings of the 10th European Conference on Machine Learning
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Roles of Macro-actions in Accelerating Reinforcement Learning TITLE2:
Roles of Macro-actions in Accelerating Reinforcement Learning TITLE2:
Improving Modeling of Other Agents using Tentative Stereotypes and Compactification of Observations
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
The Iterated Prisoners' Dilemma: 20 Years on
The Iterated Prisoners' Dilemma: 20 Years on
Overconfidence or paranoia? search in imperfect-information games
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Accelerating reinforcement learning by composing solutions of automatically identified subtasks
Journal of Artificial Intelligence Research
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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We describe how to take a set of interaction traces produced by different pairs of players in a two-player repeated game, and combine them into a composite strategy. We provide an algorithm that, in polynomial time, can generate the best such composite strategy. We describe how to incorporate the composite strategy into an existing agent, as an enhancement of the agent's original strategy. We provide experimental results using interaction traces from 126 agents (most of them written by students as class projects) for the Iterated Prisoner's Dilemma, Iterated Chicken Game, and Iterated Battle of the Sexes. We compared each agent with the enhanced version of that agent produced by our algorithm. The enhancements improved the agents' scores by about 5% in the IPD, 11% in the ICG, and 26% in the IBS, and improved their rank by about 12% in the IPD, 38% in the ICG, and 33% in the IBS.