Probabilistic Belief Change: Expansion, Conditioning and Constraining
This work addresses a theoretical gap in belief revision for AI and logic researchers, but it appears incremental as it refines existing categorizations without broad empirical validation.
The paper challenges the AGM theory's classification of belief change operations, arguing that probabilistic conditioning should not be viewed as expansion and proposing constraining as a better candidate for probabilistic expansion.
The AGM theory of belief revision has become an important paradigm for investigating rational belief changes. Unfortunately, researchers working in this paradigm have restricted much of their attention to rather simple representations of belief states, namely logically closed sets of propositional sentences. In our opinion, this has resulted in a too abstract categorisation of belief change operations: expansion, revision, or contraction. Occasionally, in the AGM paradigm, also probabilistic belief changes have been considered, and it is widely accepted that the probabilistic version of expansion is conditioning. However, we argue that it may be more correct to view conditioning and expansion as two essentially different kinds of belief change, and that what we call constraining is a better candidate for being considered probabilistic expansion.