Thinking Too Much: Pathology in Pathfinding
Luštrek Mitja, Vadim Bulitko · Frontiers in artificial intelligence and applications · 2008
Incomplete single-agent search methods are often better suited to real-time pathfinding tasks than complete methods (such as A*). Incomplete methods conduct a limited-depth lookahead search, i.e., expand a part of the space centered on the agent, and heuristically evaluate the distances from the frontier of the expanded space to the goal. Actions selected this way are not necessarily optimal, but it is generally believed that deeper lookahead increases the quality of decisions. However, in two-player games, where similar methods are used, it has long been known that this is not always the case [7, 1]. This phenomenon has been termed minimax pathology. More recently pathological behavior was discovered in single-agent search as well [3]. Some attempts to explain it have been made [5, 6], but the pathology in single-agent search is largely still not understood. In this paper we investigate lookahead pathology in real-time pathfinding on maps from commercial computer games. First, we present an empirical study showing a degree of pathology in over 90% of the problems considered. Second, we give four explanations for such wide-spread pathological behavior.