Relevance Sensitive Non-Monotonic Inference on Belief Sequences

Journal of Applied Non-Classical Logics 11 (1):131-150 (2001)
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We present a method for relevance sensitive non-monotonic inference from belief sequences which incorporates insights pertaining to prioritized inference and relevance sensitive, inconsistency tolerant belief revision. Our model uses a finite, logically open sequence of propositional formulas as a representation for beliefs and defines a notion of inference from maxiconsistent subsets of formulas guided by two orderings: a temporal sequencing and an ordering based on relevance relations between the putative conclusion and formulas in the sequence. The relevance relations are ternary (using context as a parameter) as opposed to standard binary axiomatizations. The inference operation thus defined easily handles iterated revision by maintaining a revision history, blocks the derivation of inconsistent answers from a possibly inconsistent sequence and maintains the distinction between explicit and implicit beliefs. In doing so, it provides a finitely presented formalism and a plausible model of reasoning for automated agents.



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Author Profiles

Samir Chopra
Brooklyn College (CUNY)
Rohit Parikh
CUNY Graduate Center
Konstantinos Georgatos
City University of New York

Citations of this work

Relevance in belief revision.Pavlos Peppas, Mary-Anne Williams, Samir Chopra & Norman Foo - 2015 - Artificial Intelligence 229 (C):126-138.
On AGM for Non-Classical Logics.Renata Wassermann - 2011 - Journal of Philosophical Logic 40 (2):271 - 294.
Non-prioritized ranked belief change.Samir Chopra, Aditya Ghose & Thomas Meyer - 2003 - Journal of Philosophical Logic 32 (4):417-443.

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References found in this work

On the logic of iterated belief revision.Adnan Darwiche & Judea Pearl - 1997 - Artificial Intelligence 89 (1-2):1-29.
A truth maintenance system.Jon Doyle - 1979 - Artificial Intelligence 12 (3):231-272.
Belief revision.Hans Rott - 2008 - In Jonathan Eric Adler & Lance J. Rips (eds.), Reasoning: Studies of Human Inference and its Foundations. New York: Cambridge University Press. pp. 514--534.

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