World-championship-caliber Scrabble
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
GIB: Steps Toward an Expert-Level Bridge-Playing Program
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Multiplayer games: algorithms and approaches
Multiplayer games: algorithms and approaches
Running the table: an AI for computer billiards
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The *-minimax search procedure for trees containing chance nodes
Artificial Intelligence
Optimization of a billiard player: tactical play
CG'06 Proceedings of the 5th international conference on Computers and games
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
An event-based pool physics simulator
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Optimization of a billiard player – position play
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Analysis of a winning computational billiards player
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Aiming strategy error analysis and verification of a billiard training system
Knowledge-Based Systems
Journal of Intelligent and Robotic Systems
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Billiards is a game of both strategy and physical skill. To succeed, a player must be able to select strong shots, and then execute them accurately and consistently on the table. Several robotic billiards players have recently been developed. These systems address the task of executing shots on a physical table, but so far have incorporated little strategic reasoning. They require artificial intelligence to select the 'best' shot taking into account the accuracy of the robot, the noise inherent in the domain, the continuous nature of the search space, the difficulty of the shot, and the goal of maximizing the chances of winning. This article describes the program PickPocket, the winner of the simulated 8-ball tournaments at the 10th and 11th Computer Olympiad competitions. PickPocket is based on the traditional search framework, familiar from games such as chess, adapted to the continuous stochastic domain of billiards. Experimental results are presented exploring the properties of two search algorithms, Monte-Carlo search and Probabilistic search.