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.
