Relevance Sensitive Non-Monotonic Inference on Belief Sequences

Details Relevance Sensitive Non-Monotonic Inference on Belief Sequences Relevance Sensitive Non-Monotonic Inference on Belief Sequences

2000-03-08

arxiv.org/abs/cs/0003021v1

Computer Science

Artificial Intelligence

Scientific paper

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 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.

Affiliated with

Also associated with

No associations

Rating

Relevance Sensitive Non-Monotonic Inference on Belief Sequences does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Relevance Sensitive Non-Monotonic Inference on Belief Sequences, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Relevance Sensitive Non-Monotonic Inference on Belief Sequences will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-143955