Pattern Classification in No-Limit Poker: A Head-Start Evolutionary Approach

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 poker player agent, and as such, 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 designed 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, random agents, data-driven agents, and "always fold" agents (a surprisingly effective strategy). Key words: Evolution, Pattern Classification, No-limit Hold'em, Poker.

• Type: Conference
• Year: 2007
• Where: AI
• Authors: Brien Beattie, Garrett Nicolai, David Gerhard, Robert J. Hilderman
• Keywords: AI 2007, Artificial Intelligence, Hand Value, Poker Classification, Poker Player Agent