In this paper we extend a consistency-based approach (originally introduced by Delgrande and Schaub) to belief revision for structured belief bases. We explicitly distinguish between observations, i.e., facts that an epistemic agent observes or is being told, and rules representing general knowledge about the considered world. When new information becomes available respective sets are being altered in a different way to preserve parts of knowledge during the revision process. Such an approach allows us to deal with difficult and complex scenarios, involving defeasible information and derivation filtering, with common-sense results.

We generalize the QSQR evaluation method to give the first set-oriented depth-first evaluation method for Horn knowledge bases. The resulting procedure closely simulates SLD-resolution (to take advantages of the goal-directed approach) and highly exploits set-at-a-time tabling. Our generalized QSQR evaluation procedure is sound and complete. It does not use adornments and annotations. To deal with function symbols, our procedure uses iterative deepening search, which iteratively increases term-depth bound for atoms and substitutions occurring in the computation. When the term-depth bound is fixed, our evaluation procedure runs in polynomial time in the size of extensional relations.

In this paper we generalize the MPMA belief update operator by admitting first-order knowledge bases and restricted first-order update formulae. This allows us to consider scenarios which cannot be properly formalized either in the originalMPMA or in other existing approaches to belief update.