Abstract planning with unknown object quantities and properties

Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein

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

4 Scopus citations

Abstract

State abstraction has been widely used for state aggregation in approaches to AI search and planning. In this paper we use a powerful abstraction technique from software model checking for representing collections of states with different object quantities and properties. We exploit this method to develop precise abstractions and action operators for use in AI. This enables us to find scalable, algorithm-like plans with branches and loops which can solve problems of unbounded sizes. We describe how this method of abstraction can be effectively used in AI, with compelling results from implementations of two planning algorithms.

Original languageEnglish (US)
Title of host publicationSARA 2009 - Proceedings, 8th Symposium on Abstraction, Reformulation and Approximation
Pages143-150
Number of pages8
StatePublished - Dec 1 2009
Externally publishedYes
Event8th Symposium on Abstraction, Reformulation and Approximation, SARA 2009 - Lake Arrowhead, CA, United States
Duration: Jul 7 2009Jul 10 2009

Publication series

NameSARA 2009 - Proceedings, 8th Symposium on Abstraction, Reformulation and Approximation

Other

Other8th Symposium on Abstraction, Reformulation and Approximation, SARA 2009
CountryUnited States
CityLake Arrowhead, CA
Period7/7/097/10/09

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ASJC Scopus subject areas

  • Applied Mathematics

Cite this

Srivastava, S., Immerman, N., & Zilberstein, S. (2009). Abstract planning with unknown object quantities and properties. In SARA 2009 - Proceedings, 8th Symposium on Abstraction, Reformulation and Approximation (pp. 143-150). (SARA 2009 - Proceedings, 8th Symposium on Abstraction, Reformulation and Approximation).