Anytime heuristic search for partial satisfaction planning

J. Benton, Minh Do, Subbarao Kambhampati

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


We present a heuristic search approach to solve partial satisfaction planning (PSP) problems. In these problems, goals are modeled as soft constraints with utility values, and actions have costs. Goal utility represents the value of each goal to the user and action cost represents the total resource cost (e.g., time, fuel cost) needed to execute each action. The objective is to find the plan that maximizes the trade-off between the total achieved utility and the total incurred cost; we call this problem PSP Net Benefit. Previous approaches to solving this problem heuristically convert PSP Net Benefit into STRIPS planning with action cost by pre-selecting a subset of goals. In contrast, we provide a novel anytime search algorithm that handles soft goals directly. Our new search algorithm has an anytime property that keeps returning better quality solutions until the termination criteria are met. We have implemented this search algorithm, along with relaxed plan heuristics adapted to PSP Net Benefit problems, in a forward state-space planner called SapaPS. An adaptation of SapaPS, called YochanPS, received a "distinguished performance" award in the "simple preferences" track of the 5th International Planning Competition.

Original languageEnglish (US)
Pages (from-to)562-592
Number of pages31
JournalArtificial Intelligence
Issue number5-6
StatePublished - Apr 2009


  • Heuristics
  • Partial satisfaction
  • Planning
  • Search

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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