An Accurate Adaptation-Guided Similarity Metric for Case-Based Planning

摘要

In this paper, we present an adaptation-guided similarity metric based on the estimate of the number of actions between states, called ADG (Action Distance-Guided). It is determined by using a heuristic calculation extracted from the heuristic search planning, called FF, which was the fastest planner in the AIPS'2000 competition. This heuristic provides an accurate estimate of the distance between states that is appropriated for similarity measures. Consequently, the ADG becomes a new approach, suitable for domain independent case-based planning systems that perform state-space search.