Parallel Belief Revision
This work addresses theoretical foundations for belief revision in AI, but it appears incremental as it builds upon existing Spohn systems and conditional independence frameworks.
The paper tackles the problem of formalizing belief revision by showing that Wolfgang Spohn's system has a parallel implementation derived from influence diagrams, similar to Bayesian networks, with the proof relying on completeness results for conditional independence axiomatization.
This paper describes a formal system of belief revision developed by Wolfgang Spohn and shows that this system has a parallel implementation that can be derived from an influence diagram in a manner similar to that in which Bayesian networks are derived. The proof rests upon completeness results for an axiomatization of the notion of conditional independence, with the Spohn system being used as a semantics for the relation of conditional independence.