Previous work introduced the concept of progress states. After expanding a progress state, a greedy best-first search (GBFS) will only expand states with lower heuristic values. Current methods can identify progress states only for a single task and only after a solution for the task has been found. We introduce a novel approach that learns a description logic formula characterizing all progress states in a classical planning domain. Using the learned formulas in a GBFS to break ties in favor of progress states often significantly reduces the search effort.
Funding Agencies|TAILOR; EU [952215]; DFG [389792660, TRR 248]; Eric and Wendy Schmidt Fund for Strategic Innovation; Council for Higher Education of Israel; Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; European Research Council (ERC) under the European Union [817639]