On Using Lazy Greedy Best-First Search with Subgoaling Relaxation in Numeric Planning Problems

Abstract

This paper studies the use of lazy greedy best-first search for numeric planning problems in combination with relaxation-based heuristics, helpful actions, and up-to-jumping actions. In particular, the new search schema that we study, whilst postponing evaluation of the heuristic at expansion time, focuses the search over those states that are reached by helpful and up-to-jumping actions. In addition, we revisit linear abstractions by improving the balance between computation time and information, providing guidance in non-simple numeric planning problems, too. The new search schema compares favorably over the IPC-23 benchmarks with alternative complete heuristic search planners from the literature.