TL;DR
This paper introduces a formal belief revision system by Wolfgang Spohn, demonstrating its parallel implementation via influence diagrams, akin to Bayesian networks, supported by completeness results for conditional independence.
Contribution
It presents a novel belief revision framework with a parallel computational approach derived from influence diagrams, linking it to Bayesian network methodologies.
Findings
The Spohn belief revision system can be implemented in parallel.
Influence diagrams can be used to derive the system similarly to Bayesian networks.
Completeness results support the semantics of conditional independence in this framework.
Abstract
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.
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Bayesian Modeling and Causal Inference · Semantic Web and Ontologies