GOAL-BASED ADVERSARIAL SEARCH Searching Game Trees in Complex Domains using Goal-based Heuristic

We present a novel approach to reducing adversarial search space by using background knowledge represented in the form of higher-level goals that players tend to pursue in the game. The algorithm is derived from a simultaneous-move modification of the max(n) algorithm by only searching the branches of the game tree that are consistent with pursuing player's goals. The algorithm has been tested on a real-world-based scenario modelled as a large-scale asymmetric game. The experimental results obtained indicate the ability of the goal-based heuristic to reduce the search space to a manageable level even in complex domains while maintaining the high quality of resulting strategies.