research-article

General Belief Revision

Authors:

James P. Delgrande,

Stefan WoltranAuthors Info & Claims

Journal of the ACM (JACM), Volume 65, Issue 5

Article No.: 29, Pages 1 - 34

https://doi.org/10.1145/3203409

Published: 07 September 2018 Publication History

Abstract

In artificial intelligence, a key question concerns how an agent may rationally revise its beliefs in light of new information. The standard (AGM) approach to belief revision assumes that the underlying logic contains classical propositional logic. This is a significant limitation, since many representation schemes in AI don’t subsume propositional logic. In this article, we consider the question of what the minimal requirements are on a logic, such that the AGM approach to revision may be formulated. We show that AGM-style revision can be obtained even when extremely little is assumed of the underlying language and its semantics; in fact, one requires little more than a language with sentences that are satisfied at models, or possible worlds. The classical AGM postulates are expressed in this framework and a representation result is established between the postulate set and certain preorders on possible worlds. To obtain the representation result, we add a new postulate to the AGM postulates, and we add a constraint to preorders on worlds. Crucially, both of these additions are redundant in the original AGM framework, and so we extend, rather than modify, the AGM approach. As well, iterated revision is addressed and the Darwiche/Pearl postulates are shown to be compatible with our approach. Various examples are given to illustrate the approach, including Horn clause revision, revision in extended logic programs, and belief revision in a very basic logic called literal revision.

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Cited By

Andrikopoulou MAravanis TDelgrande JKarazeris PPeppas P(2025)Filters-based revisionAnnals of Mathematics and Artificial Intelligence10.1007/s10472-024-09963-5Online publication date: 24-Jan-2025
https://doi.org/10.1007/s10472-024-09963-5
Olivieri FCristani MGovernatori GPasetto LRotolo AScannapieco STomazzoli CChekole Workneh T(2024)Revising non-monotonic theories with sufficient and necessary conditions: the case of Defeasible LogicJournal of Logic and Computation10.1093/logcom/exae044Online publication date: 6-Sep-2024
https://doi.org/10.1093/logcom/exae044
Guimarães ROzaki ARibeiro JWilliams BChen YNeville J(2023)Finite based contraction and expansion via modelsProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i5.25786(6389-6397)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i5.25786
Show More Cited By

Index Terms

General Belief Revision
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Nonmonotonic, default reasoning and belief revision
      2. Reasoning about belief and knowledge
2. Theory of computation
  1. Logic

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Published In

cover image Journal of the ACM

Journal of the ACM Volume 65, Issue 5

October 2018

299 pages

ISSN:0004-5411

EISSN:1557-735X

DOI:10.1145/3274534

Editor:
Éva Tardos
Cornell University

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Copyright © 2018 ACM.

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Association for Computing Machinery

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Publication History

Published: 07 September 2018

Accepted: 01 March 2018

Revised: 01 April 2017

Received: 01 August 2016

Published in JACM Volume 65, Issue 5

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Cited By

Andrikopoulou MAravanis TDelgrande JKarazeris PPeppas P(2025)Filters-based revisionAnnals of Mathematics and Artificial Intelligence10.1007/s10472-024-09963-5Online publication date: 24-Jan-2025
https://doi.org/10.1007/s10472-024-09963-5
Olivieri FCristani MGovernatori GPasetto LRotolo AScannapieco STomazzoli CChekole Workneh T(2024)Revising non-monotonic theories with sufficient and necessary conditions: the case of Defeasible LogicJournal of Logic and Computation10.1093/logcom/exae044Online publication date: 6-Sep-2024
https://doi.org/10.1093/logcom/exae044
Guimarães ROzaki ARibeiro JWilliams BChen YNeville J(2023)Finite based contraction and expansion via modelsProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i5.25786(6389-6397)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i5.25786
Bonanno G(2022)Filtered Belief Revision: Syntax and SemanticsJournal of Logic, Language and Information10.1007/s10849-022-09374-x31:4(645-675)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10849-022-09374-x
Falakh FRudolph SSauerwald K(2022)Semantic Characterizations of AGM Revision for Tarskian LogicsRules and Reasoning10.1007/978-3-031-21541-4_7(95-110)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-21541-4_7
Tardivo FPham LSon TPontelli E(2021)A Logic Programming Approach to Regression Based Repair of Incorrect Initial Belief StatesPractical Aspects of Declarative Languages10.1007/978-3-030-67438-0_5(73-89)Online publication date: 18-Jan-2021
https://dl.acm.org/doi/10.1007/978-3-030-67438-0_5
Zhuang ZWang ZWang KDelgrande J(2019)A generalisation of AGM contraction and revision to fragments of first-order logicJournal of Artificial Intelligence Research10.1613/jair.1.1133764:1(147-179)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1613/jair.1.11337

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