The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent learning using a variable learning rate
Artificial Intelligence
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Implicit Negotiation in Repeated Games
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
A polynomial-time Nash equilibrium algorithm for repeated games
Decision Support Systems - Special issue: The fourth ACM conference on electronic commerce
Fast concurrent reinforcement learners
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Adversary aware surveillance systems
IEEE Transactions on Information Forensics and Security
Discovery, utilization, and analysis of credible threats for 2X2 incomplete information games in TOM
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In contrast to classical game theoretic analysis of simultaneous and sequential play in bimatrix games, Steven Brams has proposed an alternative framework called the Theory of Moves (TOM) where players can choose their initial actions and then, in alternating turns, decide to shift or not from its current action. A backward induction process is used to determine a non-myopic action and equilibrium is reached when an agent, on its turn to move, decides to not change its current action. Brams claims that the TOM framework captures the dynamics of a wide range of real-life non-cooperative negotiations ranging over political, historical, and religious disputes. We believe that his analysis is weakened by the assumption that a player has perfect knowledge of the opponent's payoff. We present a learning approach by which TOM players can learn to converge to Non-Myopic Equilibria (NME) without prior knowledge of its opponent's preferences and by inducing them from past choices made by the opponent. We present experimental results from all structurally distinct 2-by-2 games without a common preferred outcome showing convergence of our proposed learning player to NMEs. We also discuss the relation between equilibriums in sequential games and NMEs of TOM.