Subset-Saturated Transition Cost Partitioning for Optimal Classical Planning

Anonymous

Keywords: optimal classical planning, heuristic search, cost partitioning

TL;DR: A heuristic for domain-independent optimal classical planning based on cost partitioning with consideration of action contexts

Abstract: Cost partitioning admissibly combines the information from multiple heuristics for state-space search. We use a greedy method called saturated cost partitioning that considers the heuristics in sequence and assigns the minimal fraction of the remaining costs that it needs to preserve the heuristic estimates. In this work, we address the problem of using more expressive transition cost functions with saturated cost partitioning to obtain stronger heuristics. Our contribution is subset-saturated transition cost partitioning that combines the concepts of using transition cost functions and prioritizing states that look more important during the search. Our empirical evaluation shows that this approach still causes too much computational overhead but leads to more informed heuristics.