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Incorporating Belief Merging into Relevance-Sensitive Belief Structures

Published: 29 March 2023 Publication History

Abstract

Belief structures have been introduced by Chopra and Parikh as a well-behaved and relevance-sensitive model that is perfectly suitable for representing the beliefs of resource-bounded agents. In this paper, we point out that belief structures are compatible with all the rationality-postulates for belief revision, proposed by Alchourrón, Gärdenfors and Makinson, combined with a popular relevance-sensitive principle, proposed independently by Parikh. This conclusion was unknown, since Chopra and Parikh considered in their original work only a portion of the rationality-postulates for belief revision. Furthermore, we incorporate the well-studied process of belief merging into the belief-structures model, and show how this approach rationally eliminates undesired inconsistencies arising when reasoning with or revising belief structures, and leads to an increased coherence of such structures.

References

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Carlos Alchourrón, Peter Gärdenfors, and David Makinson. 1985. On the logic of theory change: Partial meet contraction and revision functions. Journal of Symbolic Logic 50, 2 (1985), 510–530.
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Theofanis Aravanis, Pavlos Peppas, and Mary-Anne Williams. 2020. A study of possible-worlds semantics of relevance-sensitive belief revision. Journal of Logic and Computation 30 (2020), 1125–1142.
[3]
Theofanis I. Aravanis, Pavlos Peppas, and Mary-Anne Williams. 2019. Full characterization of Parikh’s relevance-sensitive axiom for belief revision. Journal of Artificial Intelligence Research 66 (2019), 765–792.
[4]
Samir Chopra and Rohit Parikh. 2000. Relevance sensitive belief structures. Annals of Mathematics and Artificial Intelligence 28, 1–4(2000), 259–285.
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James Delgrande and Pavlos Peppas. 2018. Incorporating relevance in epistemic states in belief revision. In Proceedings of the 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018). 230–239.
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Peter Gärdenfors. 1988. Knowledge in Flux – Modeling the Dynamics of Epistemic States. MIT Press, Cambridge, Massachusetts.
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Gabriele Kern-Isberner and Gerhard Brewka. 2017. Strong syntax splitting for iterated belief revision. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017). 1131–1137.
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Sébastien Konieczny and Ramón Pino Pérez. 2002. Merging information under constraints: A logical framework. Journal of Logic and Computation 12, 5 (2002), 773–808.
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Sébastien Konieczny and Ramón Pino Pérez. 2011. Logic based merging. Journal of Philosophical Logic 40 (2011), 239–270.
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David Makinson. 2009. Propositional relevance through letter-sharing. Journal of Applied Logic 7 (2009), 377–387.
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Rohit Parikh. 1999. Beliefs, belief revision, and splitting languages. In Logic, Language and Computation, Lawrence S. Moss, Jonathan Ginzburg, and Maarten de Rijke (Eds.). Vol. 2. CSLI Publications, 266–278.
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Rohit Parikh. 2011. Beth definability, interpolation and language splitting. Synthese 179(2011), 211–221.
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Pavlos Peppas. 2008. Belief revision. In Handbook of Knowledge Representation, Frank van Harmelen, Vladimir Lifschitz, and Bruce Porter (Eds.). Elsevier Science, 317–359.
[15]
Pavlos Peppas, Mary-Anne Williams, Samir Chopra, and Norman Foo. 2015. Relevance in belief revision. Artificial Intelligence 229 (2015), 126–138.

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PCI '22: Proceedings of the 26th Pan-Hellenic Conference on Informatics
November 2022
414 pages
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Association for Computing Machinery

New York, NY, United States

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Published: 29 March 2023

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Author Tags

  1. Belief Merging
  2. Belief Revision
  3. Belief Structures
  4. Knowledge Representation
  5. Relevance
  6. Resource-Bounded Agents

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PCI 2022
PCI 2022: 26th Pan-Hellenic Conference on Informatics
November 25 - 27, 2022
Athens, Greece

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