Reviving partial order planning

XuanLong Nguyen, Subbarao Kambhampati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

86 Scopus citations

Abstract

This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms. Our key insight is that the techniques responsible for the efficiency of the currently successful planners-viz., distance based heuristics, reachability analysis and disjunctive constraint handling-can also be adapted to dramatically improve the efficiency of the POP algorithm. We implement our ideas in a variant of UCPOP called REPOP 1. Our empirical results show that in addition to dominating UCPOP, REPOP also convincingly outperforms Graphplan in several "parallel" domains. The plans generated by REPOP also tend to be better than those generated by Graphplan and state search planners in terms of execution flexibility.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages459-464
Number of pages6
StatePublished - 2001
Event17th International Joint Conference on Artificial Intelligence, IJCAI 2001 - Seattle, WA, United States
Duration: Aug 4 2001Aug 10 2001

Other

Other17th International Joint Conference on Artificial Intelligence, IJCAI 2001
CountryUnited States
CitySeattle, WA
Period8/4/018/10/01

ASJC Scopus subject areas

  • Artificial Intelligence

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