Learning generalized plans using abstract counting

Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein

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

23 Scopus citations

Abstract

Given the complexity of planning, it is often beneficial to create plans that work for a wide class of problems. This facilitates reuse of existing plans for different instances drawn from the same problem or from an infinite family of similar problems. We define a class of such planning problems called generalized planning problems and present a novel approach for transforming classical plans into generalized plans. These algorithm-like plans include loops and work for problem instances having varying numbers of objects that must be manipulated to reach the goal. Our approach takes as input a classical plan for a certain problem instance. It outputs a generalized plan along with a classification of the problem instances where it is guaranteed to work. We illustrate the utility of our approach through results of a working implementation on various practical examples.

Original languageEnglish (US)
Title of host publicationAAAI-08/IAAI-08 Proceedings - 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference
Pages991-997
Number of pages7
StatePublished - Dec 24 2008
Externally publishedYes
Event23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 - Chicago, IL, United States
Duration: Jul 13 2008Jul 17 2008

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

Other23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08
CountryUnited States
CityChicago, IL
Period7/13/087/17/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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