Anytime heuristic search for partial satisfaction planning

J. Benton, Minh Do, Subbarao Kambhampati

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

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
Volume173
Issue number5-6
DOIs
StatePublished - Apr 2009

Fingerprint

heuristics
Planning
planning
costs
Costs
utility value
Heuristics
resources
performance
Values

Keywords

  • Heuristics
  • Partial satisfaction
  • Planning
  • Search

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Anytime heuristic search for partial satisfaction planning. / Benton, J.; Do, Minh; Kambhampati, Subbarao.

In: Artificial Intelligence, Vol. 173, No. 5-6, 04.2009, p. 562-592.

Research output: Contribution to journalArticle

Benton, J. ; Do, Minh ; Kambhampati, Subbarao. / Anytime heuristic search for partial satisfaction planning. In: Artificial Intelligence. 2009 ; Vol. 173, No. 5-6. pp. 562-592.
@article{5906cbf190ae45c69f08416519dbd0c0,
title = "Anytime heuristic search for partial satisfaction planning",
abstract = "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.",
keywords = "Heuristics, Partial satisfaction, Planning, Search",
author = "J. Benton and Minh Do and Subbarao Kambhampati",
year = "2009",
month = "4",
doi = "10.1016/j.artint.2008.11.010",
language = "English (US)",
volume = "173",
pages = "562--592",
journal = "Artificial Intelligence",
issn = "0004-3702",
publisher = "Elsevier",
number = "5-6",

}

TY - JOUR

T1 - Anytime heuristic search for partial satisfaction planning

AU - Benton, J.

AU - Do, Minh

AU - Kambhampati, Subbarao

PY - 2009/4

Y1 - 2009/4

N2 - 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.

AB - 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.

KW - Heuristics

KW - Partial satisfaction

KW - Planning

KW - Search

UR - http://www.scopus.com/inward/record.url?scp=60649092091&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=60649092091&partnerID=8YFLogxK

U2 - 10.1016/j.artint.2008.11.010

DO - 10.1016/j.artint.2008.11.010

M3 - Article

AN - SCOPUS:60649092091

VL - 173

SP - 562

EP - 592

JO - Artificial Intelligence

JF - Artificial Intelligence

SN - 0004-3702

IS - 5-6

ER -