A Novel Stable Model Computation Approach for General Dedcutive Databases

The aim of this thesis is to develop faster method for stable model computation of non-stratified logic programs and study its efficiency. It focuses mainly on the stable model and weak well founded semantics of logic programs. We propose an approach to compute stable models by where we first transform the logic program using paraconsistent relational model, then we compute the weak-well founded model which is used to generate a set of models consisting of the true and unknown values, which are tested for stability. We perform some experiments to test the efficiency of our approach which incurs overhead to eliminate negative values against a Naïve method of stable model computation

Bottom-Up Computation of the Fitting Model for General Deductive Databases

General logic programs are those that contain both positive and negative subgoals in their clause bodies. For such programs Fitting proposed an elegant 3-valued minimum model semantics that avoids some impracticalities of previous approaches. Here we present a method to compute this Fitting model for deductive databases. We introduce partial relations, which are the semantic objects associated with predicate symbols, and define algebraic operators over them. The first step in our model computation method is to convert the database rules into partial relation definitions involving these operators. The second step is to build the minimum model iteratively. We give algorithms for both steps and show their termination and correctness. We also suggest extensions to our method for computing the well-founded model proposed by van Gelder, Ross and Schlipf. 1 Introduction A deductive database consisting only of Horn clauses is guaranteed to have a unique minimum model, which is taken to be the..