Influence of anomalous truth values on logical inference in VTF-logics as a basis for verification of rule-based systems

Abstract

The paper is devoted to the problem of expert systems knowledge bases verification. The methodological basis of verification is logics with vector semantics in the VTF-logics form. The knowledge model is a rule-based system. The issues of algorithmization of contradictions and other problems detection are considered in the paper. Algorithmization is based on the characteristic features of in VTF-logics inference. It is shown that in the formalism under consideration, the anomalous truth of a small premise generates the same conclusion (a large premise is considered strictly true). Anomalies such as falsity, uncertainty, and contradiction are considered. The problems of reducing the computational complexity of algorithms are considered.