Measuring the performance potential of chess programs
Artificial Intelligence - Special issue on computer chess
Temporal difference learning and TD-Gammon
Communications of the ACM
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
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
GIB: steps toward an expert-level bridge-playing program
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
IEEE Intelligent Systems
Machines that learn to play games
Abstracting Imperfect Information Game Trees
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
An Investigation of an Adaptive Poker Player
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Machine Learning
CASPER: A Case-Based Poker-Bot
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Enhancing artificial intelligence on a real mobile game
International Journal of Computer Games Technology - Artificial Intelligence for Computer Games
Monte-Carlo simulation balancing
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Adaptive play in Texas Hold'em Poker
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
An exploitative Monte-Carlo poker agent
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Artificial Intelligence
Monte-Carlo tree search and rapid action value estimation in computer Go
Artificial Intelligence
Learning to win by reading manuals in a Monte-Carlo framework
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Similarity-Based retrieval and solution re-use policies in the game of texas hold'em
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Efficient control of selective simulations
CG'04 Proceedings of the 4th international conference on Computers and Games
Game-Tree search with adaptation in stochastic imperfect-information games
CG'04 Proceedings of the 4th international conference on Computers and Games
Current challenges in multi-player game search
CG'04 Proceedings of the 4th international conference on Computers and Games
Non-linear Monte-Carlo search in civilization II
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
SARTRE: a case-based poker web app
Proceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System
Learning to win by reading manuals in a monte-carlo framework
Journal of Artificial Intelligence Research
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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Until recently, artificial intelligence researchers who use games as their experimental testbed have concentrated on games of perfect information. Many of these games have been amenable to brute-force search techniques. In contrast, games of imperfect information, such as bridge and poker, contain hidden information making similar search techniques impractical. This paper describes recent progress in developing a high-performance pokerplaying program. The advances come in two forms. First, we introduce a new betting strategy that returns a probabilistic betting decision, a probability triple, that gives the likelihood of a fold, call or raise occurring in a given situation. This component unifies all the expert knowledge used in the program, does a better job of representing the type of decision making needed to play strong poker, and improves the way information is propagated throughout the program. Second, real-time simulations are used to compute the expected values of betting decisions. The program generates an instance of the missing data, subject to any constraints that have been learned, and then simulates the rest of the game to determine a numerical result. By repeating this a sufficient number of times, a statistically meaningful sample is used in the program's decision-making process. Experimental results show that these enhancements each represent major advances in the strength of computer poker programs.