A Model-Based Algorithm for Propositional Belief Revision


Propositional Inference and belief revision are quite relevant to Automatic Reasoning. Given a KB Σ in DF and new data Σ' in CF, we have a tendency to show a deterministic and complete linear-time algorithm to make your mind up Σ Ͷvi; Σ'. We adapt the previous algorithm to make a model-based mostly proposal for belief revision: Σ' = Σ ∘ P. Our proposal relies on Dalal's technique of building a brand new DF Σ' in keeping with p and whose models have the property to hold minimum changes with models of the first KB Σ. We have a tendency to show that, in the worst case, our proposal of belief revision involves the answer of satisfiability instances shaped by subformulas of p, which implies the answer of NP-complete issues.

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