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The implementation of a model-based belief revision system

Authors:

Timothy S. C. Chou,

Marianne WinslettAuthors Info & Claims

ACM SIGART Bulletin, Volume 2, Issue 3

Pages 28 - 34

https://doi.org/10.1145/122296.122301

Published: 01 June 1991 Publication History

Abstract

In this paper, we briefly describe Immortal, an implementation of a model-based belief revision system. We present our motivation for building Immortal, describe its basic components and properties, and discuss our method of evaluating Immortal.

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Cited By

Peppas PWilliams MAntoniou G(2024)Revision operators with compact representationsArtificial Intelligence10.1016/j.artint.2024.104080(104080)Online publication date: Feb-2024
https://doi.org/10.1016/j.artint.2024.104080
Potter S(2018)Critical reasoningApplied Intelligence10.1007/s10489-011-0331-y37:3(337-356)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10489-011-0331-y
Lundberg RRibeiro MWassermann R(2012)A framework for empirical evaluation of belief change operatorsProceedings of the 21st Brazilian conference on Advances in Artificial Intelligence10.1007/978-3-642-34459-6_2(12-21)Online publication date: 20-Oct-2012
https://dl.acm.org/doi/10.1007/978-3-642-34459-6_2
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Index Terms

The implementation of a model-based belief revision system
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Nonmonotonic, default reasoning and belief revision

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Information & Contributors

Information

Published In

cover image ACM SIGART Bulletin

ACM SIGART Bulletin Volume 2, Issue 3

Special issue on implemented knowledge representation and reasoning systems

June 1991

151 pages

ISSN:0163-5719

DOI:10.1145/122296

Editor:
Jon Doyle
MIT Lab., Cambridge, MA

Issue’s Table of Contents

Copyright © 1991 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1991

Published in SIGAI Volume 2, Issue 3

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Cited By

Peppas PWilliams MAntoniou G(2024)Revision operators with compact representationsArtificial Intelligence10.1016/j.artint.2024.104080(104080)Online publication date: Feb-2024
https://doi.org/10.1016/j.artint.2024.104080
Potter S(2018)Critical reasoningApplied Intelligence10.1007/s10489-011-0331-y37:3(337-356)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10489-011-0331-y
Lundberg RRibeiro MWassermann R(2012)A framework for empirical evaluation of belief change operatorsProceedings of the 21st Brazilian conference on Advances in Artificial Intelligence10.1007/978-3-642-34459-6_2(12-21)Online publication date: 20-Oct-2012
https://dl.acm.org/doi/10.1007/978-3-642-34459-6_2
Minchin RPorto FVangenot CHartmann S(2006)Symptoms Ontology for Mapping Diagnostic Knowledge SystemsProceedings of the 19th IEEE Symposium on Computer-Based Medical Systems10.1109/CBMS.2006.152(593-598)Online publication date: 22-Jun-2006
https://dl.acm.org/doi/10.1109/CBMS.2006.152
Bai YVaradharajan V(2005)A language for specifying sequences of authorization transformations and its applicationsInformation and Communications Security10.1007/BFb0028460(39-49)Online publication date: 17-Jun-2005
https://doi.org/10.1007/BFb0028460
Bai YVaradharajan V(2005)Analysis and implementation of a formal authorization policy design approachInformation Security and Privacy10.1007/BFb0027939(14-25)Online publication date: 16-Jun-2005
https://doi.org/10.1007/BFb0027939
Bai YVaradharajan VPasquini A(1999)On Formal Languages for Sequences of Authorization TransformationsComputer Safety, Reliability and Security10.1007/3-540-48249-0_32(375-384)Online publication date: 14-Oct-1999
https://doi.org/10.1007/3-540-48249-0_32
Herzig A(1998)Logics for Belief Base UpdatingBelief Change10.1007/978-94-011-5054-5_5(189-231)Online publication date: 1998
https://doi.org/10.1007/978-94-011-5054-5_5

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