A note on paraconsistent entailment in machine learning

Recent publications witness that there is a growing interest in multi-valued logics for machine learning; some of them arose as a more or less formal description of a computer program's inferential behaviour. The referred origin of these systems is Belnap's fourvalued logic, which has been adopted for the various needs of knowledge representation in a machine learning system. However, it is unclear what an inconsistent knowledge base entails. We investigate Mobal's logic < and show how to interpret the term `paraconsistent inference' of this system. It turns out that the meaning of the basic connective ! of < can be represented as a combination of two systems of Kleene's strong three-valued logic, where the two systems differ in the set of designated truth values. The resulting logic is functionally complete but the entailment relation is not axiomatizable. This drawback yields a fundamental difference between nonmontonicity within belief-revision and non-monotonic reasoning systems like Servi's refinement 1 of Gabbay's