A Knowledge-Based Framework for Belief Change, Part II: Revision and Update

Nir Friedman; Joseph Y. Halpern

A Knowledge-Based Framework for Belief Change, Part II: Revision and Update

Nir Friedman
, Joseph Y. Halpern

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

34 Scopus citations

Abstract

The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper [FH94b] we introduced a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the changes of beliefs over time. In this paper we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method and to better understand the principles underlying them. In particular, it allows us to understand the source of Gardenfors' triviality result for belief revision [Gär86] and suggests a way of mitigating the problem. It also shows that Katsuno and Mendelzon's notion of belief update [KM91a] depends on several strong assumptions that may limit its applicability in AI.

Original language	English
Title of host publication	Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, KR 1994
Editors	Jon Doyle, Erik Sandewall, Pietro Torasso
Publisher	Association for the Advancement of Artificial Intelligence
Pages	190-201
Number of pages	12
ISBN (Electronic)	155860328X, 9781558603288
State	Published - 1994
Externally published	Yes
Event	4th International Conference on Principles of Knowledge Representation and Reasoning, KR 1994 - Bonn, Germany Duration: 24 May 1994 → 27 May 1994

Publication series

Name	Proceedings of the International Conference on Knowledge Representation and Reasoning
ISSN (Print)	2334-1025
ISSN (Electronic)	2334-1033

Conference

Conference	4th International Conference on Principles of Knowledge Representation and Reasoning, KR 1994
Country/Territory	Germany
City	Bonn
Period	24/05/94 → 27/05/94

Bibliographical note

Publisher Copyright:
Copyright © 1994 by Morgan Kaufmann Publishers, Inc. All rights reserved.

Cite this

Friedman, N., & Halpern, J. Y. (1994). A Knowledge-Based Framework for Belief Change, Part II: Revision and Update. In J. Doyle, E. Sandewall, & P. Torasso (Eds.), Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, KR 1994 (pp. 190-201). (Proceedings of the International Conference on Knowledge Representation and Reasoning). Association for the Advancement of Artificial Intelligence.

Friedman, Nir ; Halpern, Joseph Y. / A Knowledge-Based Framework for Belief Change, Part II : Revision and Update. Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, KR 1994. editor / Jon Doyle ; Erik Sandewall ; Pietro Torasso. Association for the Advancement of Artificial Intelligence, 1994. pp. 190-201 (Proceedings of the International Conference on Knowledge Representation and Reasoning).

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abstract = "The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper [FH94b] we introduced a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the changes of beliefs over time. In this paper we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method and to better understand the principles underlying them. In particular, it allows us to understand the source of Gardenfors' triviality result for belief revision [G{\"a}r86] and suggests a way of mitigating the problem. It also shows that Katsuno and Mendelzon's notion of belief update [KM91a] depends on several strong assumptions that may limit its applicability in AI.",

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Friedman, N & Halpern, JY 1994, A Knowledge-Based Framework for Belief Change, Part II: Revision and Update. in J Doyle, E Sandewall & P Torasso (eds), Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, KR 1994. Proceedings of the International Conference on Knowledge Representation and Reasoning, Association for the Advancement of Artificial Intelligence, pp. 190-201, 4th International Conference on Principles of Knowledge Representation and Reasoning, KR 1994, Bonn, Germany, 24/05/94.

A Knowledge-Based Framework for Belief Change, Part II: Revision and Update. / Friedman, Nir; Halpern, Joseph Y.
Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, KR 1994. ed. / Jon Doyle; Erik Sandewall; Pietro Torasso. Association for the Advancement of Artificial Intelligence, 1994. p. 190-201 (Proceedings of the International Conference on Knowledge Representation and Reasoning).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper [FH94b] we introduced a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the changes of beliefs over time. In this paper we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method and to better understand the principles underlying them. In particular, it allows us to understand the source of Gardenfors' triviality result for belief revision [Gär86] and suggests a way of mitigating the problem. It also shows that Katsuno and Mendelzon's notion of belief update [KM91a] depends on several strong assumptions that may limit its applicability in AI.

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Friedman N, Halpern JY. A Knowledge-Based Framework for Belief Change, Part II: Revision and Update. In Doyle J, Sandewall E, Torasso P, editors, Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning, KR 1994. Association for the Advancement of Artificial Intelligence. 1994. p. 190-201. (Proceedings of the International Conference on Knowledge Representation and Reasoning).

A Knowledge-Based Framework for Belief Change, Part II: Revision and Update

Abstract

Publication series

Conference

Bibliographical note

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Cite this