A Consistency-Based Model for Belief Change: Preliminary Report
  We present a general, consistency-based framework for belief change.
Informally, in revising K by A, we begin with A and incorporate as much of K as
consistently possible. Formally, a knowledge base K and sentence A are
expressed, via renaming propositions in K, in separate languages. Using a
maximization process, we assume the languages are the same insofar as
consistently possible. Lastly, we express the resultant knowledge base in a
single language. There may be more than one way in which A can be so extended
by K: in choice revision, one such ``extension'' represents the revised state;
alternately revision consists of the intersection of all such extensions.
  The most general formulation of our approach is flexible enough to express
other approaches to revision and update, the merging of knowledge bases, and
the incorporation of static and dynamic integrity constraints. Our framework
differs from work based on ordinal conditional functions, notably with respect
to iterated revision. We argue that the approach is well-suited for
implementation: the choice revision operator gives better complexity results
than general revision; the approach can be expressed in terms of a finite
knowledge base; and the scope of a revision can be restricted to just those
propositions mentioned in the sentence for revision A.
