Approaches to computer game playing based on (typically α-β) search of the tree of possible move sequences combined with an evaluation function have been successful for many games, notably Chess. For games with large search spaces and complex positions, such as Go, these approaches are less successful and we are led to seek alternative approaches.

One such alternative is to model the goals of the players, and their strategies for achieving these goals. This approach means searching the space of possible goal expansions, typically much smaller than the space of move sequences.

In this paper we describe how adversarial hierarchical task network planning can provide a framework for goal-directed game playing, and its application to the game of Go.