Permissive planning: a machine learning approach to linking internal and external worlds

Gerald DeJong, Scott Bennett

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

Abstract

Because complex real-world domains defy perfect formalization, real-world planners must be able to cope with incorrect domain knowledge. This paper offers a theoretical framework for permissive planning, a machine learning method for improving the real-world behavior of planners. Permissive planning aims to acquire techniques that tolerate the inevitable mismatch between the planner's internal beliefs and the external world. Unlike the reactive approach to this mismatch, permissive planning embraces projection. The method is both problem-independent and domain-independent. Unlike classical planning, permissive planning does not exclude real-world performance from the formal definition of planning.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAmerican Association for Artificial Intelligence (AAAI) Press
Pages508-513
Number of pages6
ISBN (Print)0262510715
StatePublished - 1993
EventProceedings of the 11th National Conference on Artificial Intelligence - Washington, DC, USA
Duration: Jul 11 1993Jul 15 1993

Publication series

NameProceedings of the National Conference on Artificial Intelligence

Other

OtherProceedings of the 11th National Conference on Artificial Intelligence
CityWashington, DC, USA
Period7/11/937/15/93

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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