효율적인 계획 수립을 위한 동작-기반의 휴리스틱

Abstract

More informative ones of heuristics can help to conduct search more efficiently to obtain solution plan. However, in general, to derive highly informative heuristics from problem specifications requires lots of computational effort. To address this problem, we propose an State-Action based Planning Graph(SAPG) and Action-based heuristics for solving planning problems more efficiently. The SAPG is an extended one to be applied to can find interactions between subgoal & goal conditions from the relaxed planning graph which is a common means to get heuristics for solving the planning problems, Action-based heuristics utilizing SAPG graphs can find interactions between subgoal & goal conditions in an effective way, and then consider them to estimate the goal distance. Therefore Action-based heuristics have more information than the existing max and additive heuristics, also requires less computational effort than the existing overlap heuristics. In this pager. we present the algorithm to compute Action-based heuristics, and then explain empirical analysis to investigate the accuracy and the efficiency of the Action-based heuristics.