We consider the problem of reasoning from inconsistent hybrid theories, i.e., combinations of a structural part given by a classical first order theory (e.g., an ontology) and a rules part as a set of declarative logic program rules (under answer-set semantics). Paraconsistent reasoning is achieved by defining an appropriate semantics, so-called paraconsistent semi-equilibrium model semantics for such hybrid theories. Appropriateness of the semantics is established with respect to desirable properties attesting design objectives, such us to generalize the underlying semantics in case of consistency, as well as to generalize existing paraconsistent semantics for the individual parts. A complexity analysis of corresponding reasoning tasks complements these results.

The problem of possible applications Polymetric Analysis for the resolution problems of artificial Intelligence is discussed. As example the hybrid super intelligence system by N. Moiseev type was selected. The bond between polymetric analysis and hybrid super intelligence system was shown. In operational sense polymetric analysis is more general system. Therefore main principles of Moiseev concept may be unify with the help of polymetric analysis. Main peculiarities of this unification are analyzed.

We consider the problem of reasoning from inconsistent hybrid theories, i.e., combinations of a structural part given by a classical first order theory (e.g., an ontology) and a rules part as a set of declarative logic program rules (under answer-set semantics). Paraconsistent reasoning is achieved by defining an appropriate semantics, so-called paraconsistent semi-equilibrium model semantics for such hybrid theories. Appropriateness of the semantics is established with respect to desirable properties attesting design objectives, such us to generalize the underlying semantics in case of consistency, as well as to generalize existing paraconsistent semantics for the individual parts. A complexity analysis of corresponding reasoning tasks complements these results.

In the literature on reasoning on the basis of evidence, two traditions exist: one argument-based, and one based on narratives. Recently, we have proposed a hybrid perspective in which argumentation and narratives are combined. This formalized hybrid theory has been tested in a sense-making software prototype for criminal investigators and decision makers. In the present paper, we elaborate on the role of commonsense knowledge. We argue that two kinds of knowledge are essential: argumentation schemes and story schemes. We discuss some of the research issues that need to be addressed.