Goal-Based Game Tree Search for Complex Domains

We present a novel approach to reducing adversarial search space by employing 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 limiting the search to those 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.