A world championship caliber checkers program
Artificial Intelligence
Coevolutionary search among adversaries
Coevolutionary search among adversaries
Co-Evolution in the Successful Learning of Backgammon Strategy
Machine Learning
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Programming backgammon using self-teaching neural nets
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
GP-EndChess: using genetic programming to evolve chess endgame players
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Artificial Intelligence
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In order for computer Poker agents to play the game well, they must analyse their current quality despite imperfect information, predict the likelihood of future game states dependent upon random outcomes, model opponents who are deliberately trying to mislead them, and manage finances to improve their current condition. This leads to a game space that is large compared to other classic games such as Chess and Backgammon. Evolutionary methods have been shown to find relatively good results in large state spaces, and neural networks have been shown to be able to find solutions to non-linear search problems such as Poker. In this paper, we develop No-Limit Texas Hold'em Poker agents using a hybrid method known as evolving neural networks. We also investigate the appropriateness of evolving these agents using evolutionary heuristics such as co-evolution and halls of fame. Our agents were experimentally evaluated against several benchmark agents as well as agents previously developed in other work. Experimental results show the overall best performance was obtained by an agent evolved from a single population (i.e., no co-evolution) using a large hall of fame. These results demonstrate an effective use of evolving neural networks to create competitive No-Limit Texas Hold'em Poker agents.