Timothy T. Huang
Computer programs for the game of Go play only at the level of an advanced beginning player. The standard approach to constructing a program based on brute force game-tree search does not work well because of the game tree size and, more significantly, the difficulty in constructing fast, accurate heuristic evaluation functions. In this paper, we consider the use of intent inference in a Go program. In particular, we discuss how models of an opponent’s long-term playing style and short-term intentions can direct the exploration of candidate moves and influence the evaluation of game positions. We propose a probabilistic approach to user modeling and intent inference, and we note key issues relevant to the implementation of an intent inference agent.