Abstract
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.
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Beattie, B., Nicolai, G., Gerhard, D., Hilderman, R.J. (2007). Pattern Classification in No-Limit Poker: A Head-Start Evolutionary Approach. In: Kobti, Z., Wu, D. (eds) Advances in Artificial Intelligence. Canadian AI 2007. Lecture Notes in Computer Science(), vol 4509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72665-4_18
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DOI: https://doi.org/10.1007/978-3-540-72665-4_18
Publisher Name: Springer, Berlin, Heidelberg
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