An AGM-based belief revision mechanism for probabilistic spatio-temporal logics

Austin Parker, Guillaume Infantes, V. S. Subrahmanian, John Grant*

*Corresponding author for this work

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

5 Scopus citations

Abstract

There is now extensive interest in reasoning about moving objects. A PST knowledge base is a set of PST-atoms which are statements of the form "Object o is/was/will be at location L at time t with probability in the interval [L,U]". In this paper, we study mechanisms for belief revision in PST-KBs. We propose multiple methods for revising PST-KBs. These methods involve finding maximally consistent subsets, as well as changing the spatial, temporal, and probabilistic components of the atoms. We show that some methods cannot satisfy the AGM axioms for belief revision, while others do but are coNP-hard. Finally we present an algorithm for revision through probability change which runs in polynomial time and satisfies the AGM axioms.

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
Pages511-516
Number of pages6
StatePublished - 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
Volume1

Other

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

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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