Kasparov versus deep blue: computer chess comes of age
Kasparov versus deep blue: computer chess comes of age
Finding optimal strategies for imperfect information games
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
A Texas Hold'em poker player based on automated abstraction and real-time equilibrium computation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Algorithms and assessment in computer poker
Algorithms and assessment in computer poker
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Monte-Carlo Tree Search in Poker Using Expected Reward Distributions
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Building a no limit texas hold'em poker agent based on game logs using supervised learning
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
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Researching into the incomplete information games (IIG) field requires the development of strategies which focus on optimizing the decision making process, as there is no unequivocal best choice for a particular play. As such, this paper describes the development process and testing of an agent able to compete against human players on Poker --- one of the most popular IIG. The used methodology combines pre-defined opponent models with a reinforcement learning approach. The decision-making algorithm creates a different strategy against each type of opponent by identifying the opponent's type and adjusting the rewards of the actions of the corresponding strategy. The opponent models are simple classifications used by Poker experts. Thus, each strategy is constantly adapted throughout the games, continuously improving the agent's performance. In light of this, two agents with the same structure but different rewarding conditions were developed and tested against other agents and each other. The test results indicated that after a training phase the developed strategy is capable of outperforming basic/intermediate playing strategies thus validating this approach.