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.
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.
Abstract. In this paper, we propose a new belief revision operator, together with a method of its calculation. Our formalization differs from most of the traditional approaches in two respects. Firstly, we formally distinguish between defeasible observations and indefeasible knowledge about the considered world. In particular, our operator is differently specified depending on whether an input formula is an observation or a piece of knowledge. Secondly, we assume that a new observation, but not a new piece of knowledge, describes exactly what a reasoning agent knows at the moment about the aspect of the world the observation concerns.
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