Relevance Sensitive Non-Monotonic Inference on Belief Sequences
<|reference_start|>Relevance Sensitive Non-Monotonic Inference on Belief Sequences: 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.<|reference_end|>
@article{chopra2000relevance,
    title={Relevance Sensitive Non-Monotonic Inference on Belief Sequences},
    author={Samir Chopra, Konstantinos Georgatos, Rohit Parikh},
    journal={arXiv preprint arXiv:cs/0003021},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0003021},
    primaryClass={cs.AI}
}