Article

Free access

On the logic of iterated belief revision

Authors:

Adnan Darwiche,

Judea PearlAuthors Info & Claims

TARK '94: Proceedings of the 5th conference on Theoretical aspects of reasoning about knowledge

Pages 5 - 23

Published: 13 March 1994 Publication History

PDF eReader

Abstract

We show in this paper that the AGM postulates are too weak to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by augmenting the AGM system with four additional postulates, which are sound relative to a qualitative version of probabilistic conditioning. Finally, we establish a model-based representation theorem which characterizes the augmented system of postulates and constrains, in turns, the way in which entrenchment orderings may be transformed under iterated belief revisions.

References

[1]

Carlos Alchourron, Peter Gardenfors, and David Makinson. On the logic of theory change: Partial meet functions for contraction and revision. Journal of Symbolic Logic, 50:510--530, 1985.

Crossref

Google Scholar

[2]

Craig Boutilier. Revision sequences and nested conditionals. In Proceedings of International Joint Conference on Artifical Intelligence (IJCAI), 1993.

Google Scholar

[3]

Adnan Darwiche and Judea Pearl. Iterated belief revision using graded evidence. (Working paper), 1993.

Google Scholar

[4]

Peter Gardenfors. KNOWLEDGE IN FLUX: Modeling the Dynamics of Epistemic States. The MIT press, 1988.

Google Scholar

[5]

Moisés Goldszmidt. Qualitative probabilities: A normative framework for commonsense reasoning. Technical Report R-190, University of California at Los Angeles, Ph.D. thesis, 1992.

Digital Library

Google Scholar

[6]

Moisés Goldszmidt and Judea Pearl. Reasoning with qualitative probabilities can be tractable. In Proceedings of the 8th Conference on Uncertainty in AI, pages 112--120, Stanford, 1992.

Digital Library

Google Scholar

[7]

H. Katsuno and A. Mendelzon. Propositional knowledge base revision and minimal change. Artificial Intelligence, 52(3):263--294, 1991.

Digital Library

Google Scholar

[8]

Wolfgang Spohn. Ordinal conditional functions: A dynamic theory of epistemic states. Causation in Decision, Belief Change, and Statistics; W. L. Harper and B. Skyrms, eds., 2:105--134, 1987.

Google Scholar

[9]

Wolfgang Spohn. A general non-probabilistic theory of inductive reasoning. In L. Kanal, R. Shachter, T. Levitt, and J. Lemmer, editors, Uncertainty in Artificial Intelligence 4, pages 149--158. Elsevier Science Publishers, 1990.

Digital Library

Google Scholar

Cited By

View all

Aravanis TPeppas PWilliams M(2019)Observations on darwiche and pearl's approach for iterated belief revisionProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367248(1509-1515)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367243.3367248
(2017)Impossibility in belief mergingArtificial Intelligence10.1016/j.artint.2017.06.003251:C(1-34)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1016/j.artint.2017.06.003
Díaz APérez R(2011)Logic-based fusion of complex epistemic statesProceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty10.5555/2026067.2026106(398-409)Online publication date: 29-Jun-2011
https://dl.acm.org/doi/10.5555/2026067.2026106
Show More Cited By

Recommendations

A conditional logic for iterated belief revision
ECAI'00: Proceedings of the 14th European Conference on Artificial Intelligence

In this paper we propose a conditional logic IBC to represent iterated belief revision. We define an iterated belief revision system by strengthening the postulates proposed by Darwiche and Pearl [3]. First, following the line of Darwiche and Pearl, we ...
Iterated belief revision, revised

The AGM postulates for belief revision, augmented by the DP postulates for iterated belief revision, provide widely accepted criteria for the design of operators by which intelligent agents adapt their beliefs incrementally to new information. These ...
Believability based iterated belief revision
PRICAI'04: Proceedings of the 8th Pacific Rim International Conference on Trends in Artificial Intelligence

Classical iterated belief revision methods rarely take into account the impact of the uncertain information. In this paper, an approach of believability based iterated belief revision(BIBR) is presented. BIBR relates the belief revision in the multi-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

TARK '94: Proceedings of the 5th conference on Theoretical aspects of reasoning about knowledge

March 1994

348 pages

ISBN:155860331X

Editor:
Ronald Fagin
IBM Almaden Research Center, San Jose, CA

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 13 March 1994

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 61 of 177 submissions, 34%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
308
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)2

Reflects downloads up to 28 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Aravanis TPeppas PWilliams M(2019)Observations on darwiche and pearl's approach for iterated belief revisionProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367243.3367248(1509-1515)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367243.3367248
(2017)Impossibility in belief mergingArtificial Intelligence10.1016/j.artint.2017.06.003251:C(1-34)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1016/j.artint.2017.06.003
Díaz APérez R(2011)Logic-based fusion of complex epistemic statesProceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty10.5555/2026067.2026106(398-409)Online publication date: 29-Jun-2011
https://dl.acm.org/doi/10.5555/2026067.2026106
Jin YThielscher M(2007)Iterated belief revision, revisedArtificial Intelligence10.1016/j.artint.2006.11.002171:1(1-18)Online publication date: 1-Jan-2007
https://dl.acm.org/doi/10.1016/j.artint.2006.11.002
Van Der Westhuizen PVan Der Poll JLabuschagne W(2006)Templated revisionProceedings of the 2006 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries10.1145/1216262.1216286(218-229)Online publication date: 9-Oct-2006
https://dl.acm.org/doi/10.1145/1216262.1216286
Jin YThielscher M(2005)Iterated belief revision, revisedProceedings of the 19th international joint conference on Artificial intelligence10.5555/1642293.1642370(478-483)Online publication date: 30-Jul-2005
https://dl.acm.org/doi/10.5555/1642293.1642370
Yang PGao YChen ZChen S(2004)Believability based iterated belief revisionProceedings of the 8th Pacific Rim International Conference on Trends in Artificial Intelligence10.1007/978-3-540-28633-2_102(936-937)Online publication date: 9-Aug-2004
https://dl.acm.org/doi/10.1007/978-3-540-28633-2_102
Nayak APagnucco MPeppas P(2003)Dynamic belief revision operatorsArtificial Intelligence10.1016/S0004-3702(03)00017-1146:2(193-228)Online publication date: 1-Jun-2003
https://dl.acm.org/doi/10.1016/S0004-3702%2803%2900017-1
Chopra SParikh R(1999)An inconsistency tolerant model for belief representation and belief revisionProceedings of the 16th international joint conference on Artifical intelligence - Volume 110.5555/1624218.1624247(192-197)Online publication date: 31-Jul-1999
https://dl.acm.org/doi/10.5555/1624218.1624247
Dubois DPrade H(1996)Belief revision with uncertain inputs in the possibilistic settingProceedings of the Twelfth international conference on Uncertainty in artificial intelligence10.5555/2074284.2074312(236-243)Online publication date: 1-Aug-1996
https://dl.acm.org/doi/10.5555/2074284.2074312
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Recommendations

A conditional logic for iterated belief revision

Iterated belief revision, revised

Believability based iterated belief revision

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations