Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Artificial Intelligence
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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We have constructed a poker classification system which makes informed betting decisions based upon three defining features extracted while playing poker: hand value, risk, and aggressiveness. The system is implemented as a player-agent, therefore the goals of the classifier are not only to correctly determine whether each hand should be folded, called, or raised, but to win as many chips as possible from the other players. The decision space is found by evolutionary methods, starting from a data-driven initial state. Our results showed that evolving an agent from a data-driven "head-start" position resulted in the best performance over agents evolved from scratch, data-driven agents, random agents, and "always fold" agents.