Perspectives on multiagent learning
Artificial Intelligence
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Effective short-term opponent exploitation in simplified poker
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Bayes-relational learning of opponent models from incomplete information in no-limit poker
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Gradient-based algorithms for finding Nash equilibria in extensive form games
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Proceedings of the 13th ACM Conference on Electronic Commerce
Online implicit agent modelling
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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We develop an algorithm for opponent modeling in large extensive-form games of imperfect information. It works by observing the opponent's action frequencies and building an opponent model by combining information from a precomputed equilibrium strategy with the observations. It then computes and plays a best response to this opponent model; the opponent model and best response are both updated continually in real time. The approach combines game-theoretic reasoning and pure opponent modeling, yielding a hybrid that can effectively exploit opponents after only a small number of interactions. Unlike prior opponent modeling approaches, ours is fundamentally game theoretic and takes advantage of recent algorithms for automated abstraction and equilibrium computation rather than relying on domain-specific prior distributions, historical data, or a handcrafted set of features. Experiments show that our algorithm leads to significantly higher win rates (than an approximate-equilibrium strategy) against several opponents in limit Texas Hold'em --- the most studied imperfect-information game in computer science --- including competitors from recent AAAI computer poker competitions.