Domain independent approaches for finding diverse plans

Biplav Srivastava, Tuan A. Nguyen, Alfonso Gerevini, Subbarao Kambhampati, Minh Binh Do, Ivan Serina

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

45 Scopus citations

Abstract

In many planning situations, a planner is required to return a diverse set of plans satisfying the same goals which will be used by the external systems collectively. We take a domain-independent approach to solving this problem. We propose different domain independent distance functions among plans that can provide meaningful insights about the diversity in the plan set. We then describe how two representative state-of-the-art domain independent planning approaches - one based on compilation to CSP, and the other based on heuristic local search - can be adapted to produce diverse plans. We present empirical evidence demonstrating the effectiveness of our approaches.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages2016-2022
Number of pages7
StatePublished - 2007
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: Jan 6 2007Jan 12 2007

Other

Other20th International Joint Conference on Artificial Intelligence, IJCAI 2007
CountryIndia
CityHyderabad
Period1/6/071/12/07

ASJC Scopus subject areas

  • Artificial Intelligence

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    Srivastava, B., Nguyen, T. A., Gerevini, A., Kambhampati, S., Do, M. B., & Serina, I. (2007). Domain independent approaches for finding diverse plans. In IJCAI International Joint Conference on Artificial Intelligence (pp. 2016-2022)