State-based regression with sensing and knowledge

Richard Scherl, Cao Son Tran, Chitta Baral

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

Abstract

This paper develops a state-based regression method for planning domains with sensing operators and a representation of the knowledge of the planning agent. The language includes primitive actions, sensing actions, and conditional plans. We prove the soundness and completeness of the regression formulation with respect to the definition of progression and the semantics of a propositional modal logic of knowledge. It is our expectation that this work will serve as the foundation for the extension of recently successful work on state-based regression planning to include sensing and knowledge as well.

Original languageEnglish (US)
Title of host publicationPRICAI 2008
Subtitle of host publicationTrends in Artificial Intelligence - 10th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Pages345-357
Number of pages13
DOIs
StatePublished - Dec 1 2008
Event10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008 - Hanoi, Viet Nam
Duration: Dec 15 2008Dec 19 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5351 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008
CountryViet Nam
CityHanoi
Period12/15/0812/19/08

Keywords

  • Knowledge
  • Plans
  • Regression
  • Sensing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Scherl, R., Tran, C. S., & Baral, C. (2008). State-based regression with sensing and knowledge. In PRICAI 2008: Trends in Artificial Intelligence - 10th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 345-357). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5351 LNAI). https://doi.org/10.1007/978-3-540-89197-0_33