Action Elimination and Plan Neighborhood Graph Search: Two Algorithms for Plan Improvement - Extended Version

• Author(s) / Creator(s)

  • Nakhost, Hootan
  • Müller, Martin

• Technical report TR10-01. Compared to optimal planners, satisficing planners can solve much harder problems but may produce overly costly and long plans. Plan quality for satisficing planners has become increasingly important. The most recent planning competition IPC-2008 used the cost of the best known plan divided by the cost of the generated plan as an evaluation metric. This paper proposes and evaluates two simple but effective methods for plan improvement: Action Elimination improves an existing plan by repeatedly removing sets of irrelevant actions. Plan Neighborhood Graph Search finds a new, shorter plan by creating a plan neighborhood graph PNG(π ) of a given plan π, and then extracts a shortest path from PNG(π). Both methods are implemented in the ARAS postprocessor and are empirically shown to improve the re- sult of several planners, including the top four planners from IPC-2008, under competition conditions. | TRID-ID TR10-01

• Date created

  2010

• Subjects / Keywords

  • Artificial Intelligence
  • Action Elimination
  • Plan improvement
  • Planning
  • Plan Neighborhood Graph Search

• Type of Item

  Report

• DOI

  https://doi.org/10.7939/R3FM9H

• License

  Attribution 3.0 International