Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Representations and solutions for game-theoretic problems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Studies in machine cognition using the game of poker
Communications of the ACM
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Evolving explicit opponent models in game playing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Bidding decision model for the card game Tarok
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
CASPER: A Case-Based Poker-Bot
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Effective short-term opponent exploitation in simplified poker
Machine Learning
Effective short-term opponent exploitation in simplified poker
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Artificial Intelligence
RVAB: rational varied-depth search in Siguo game
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Game-Tree search with adaptation in stochastic imperfect-information games
CG'04 Proceedings of the 4th international conference on Computers and Games
Opponent's style modeling based on situations for bayesian poker
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Poker is ideal for testing automated reasoning under uncertainty. It introduces uncertainty both by physical randomization and by incomplete information about opponents' hands. Another source of uncertainty is the limited information available to construct psychological models of opponents, their tendencies to bluff, play conservatively, reveal weakness, etc. and the relation between their hand strengths and betting behaviour. All of these uncertainties must be assessed accurately and combined effectively for any reasonable level of skill in the game to be achieved, since good decision making is highly sensitive to those tasks. We describe our Bayesian Poker Program (BPP), which uses a Bayesian network to model the program's poker hand, the opponent's hand and the opponent's playing behaviour conditioned upon the hand, and betting curves which govern play given a probability of winning. The history of play with opponents is used to improve BPP's understanding of their behaviour. We compare BPP experimentally with: a simple rule-based system; a program which depends exclusively on hand probabilities (i.e., without opponent modeling); and with human players. BPP has shown itself to be an effective player against all these opponents, barring the better humans. We also sketch out some likely ways of improving play.