PickPocket: A computer billiards shark

Abstract

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.