C4.5: programs for machine learning
C4.5: programs for machine learning
Multiagent learning using a variable learning rate
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Strategic interactions among agents with bounded rationality
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper studies repeated interactions between an agent and an unknown opponent that changes its strategy over time. We propose a framework for learning switching non-stationary strategies. The approach uses decision trees to learn the most up to date opponent's strategy. Then, the agent's strategy is computed by transforming the tree into a Markov Decision Process (MDP), whose solution dictates the optimal way of playing against the learned strategy. The agent's learnt model is continuously re-evaluated to assess strategy switches. Our method detects such strategy switches by measuring tree similarities, and reveals whether the opponent has changed its strategy and a new model has to be learned. We evaluated the proposed approach in the iterated prisoner's dilemma, outperforming common strategies against stationary and non-stationary opponents.