Opponent-model search in Bao: Conditions for a successful application

Abstract

Opponent-Model search is a game-tree search method that explicitly uses knowledge of the opponent. There is some risk involved in using Opponent-Model search. Both the prediction of the opponent's moves and the estimation of the profitability of future positions should be of good quality and as such they should obey certain conditions. To investigate the role of prediction and estimation in actual computer game-playing, experiments with Opponent-Model search were performed in the game of Bao. After fi ve evaluation functions had been generated using machine-learning techniques, a series of tournaments between these evaluation functions was executed. They showed that Opponent-Model search can be applied successfully, provided that the conditions are met. © 2004 by Springer Science+Business Media New York.