Relevance Sensitive Non-Monotonic Inference on Belief Sequences

Journal of Applied Non-Classical Logics 11 (1-2):131-150 (2001)

Samir Chopra
Brooklyn College (CUNY)
Konstantinos Georgatos
City University of New York
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.
Keywords Knowledge representation  belief revision  belief bases   iterated revision  non-monotonic inference  relevance
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DOI 10.3166/jancl.11.131-150
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References found in this work BETA

On the Logic of Iterated Belief Revision.Adnan Darwiche & Judea Pearl - 1997 - Artificial Intelligence 89:1-29.
Belief Revision.Hans Rott - 1995 - In Jonathan Eric Adler & Lance J. Rips (eds.), Reasoning: Studies of Human Inference and its Foundations. Cambridge University Press. pp. 514--534.
Resource Bounded Belief Revision.Renata Wassermann - 1999 - Erkenntnis 50 (2-3):429-446.

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On AGM for Non-Classical Logics.Renata Wassermann - 2011 - Journal of Philosophical Logic 40 (2):271 - 294.

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