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

Year: 2025

Type: conference-paper

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.

Source: Proceedings of the International Conference on Automated Planning and Scheduling

DOI: 10.1609/icaps.v35i1.36125

Authors: Enrico Scala, Luigi Bonassi

Institutions: University of Brescia, University of Oxford

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: UK Research and Innovation

OpenAlex ID: W4414229875

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