Presently successful applications areas for ILP systems include the learning of structure-activity rules for drug design, finite-element mesh analysis design rules, primary-secondary prediction of protein structure and fault diagnosis rules for satellites.

In Proc. Workshop on Algorithmic Learning Theory, pp. 368–381.

This paper demonstrates how A-Prolog can be used to solve the problem of nonmonotonic inductive learning in the context of the learning of the behavior of dynamic domains. Nonmonotonic inductive learning is an extension of traditional inductive learning, characterized by the use of default negation in the background knowledge and/or in the clauses being learned. The importance of nonmonotonic inductive learning lies in the fact that it allows to learn theories containing defaults and ultimately to help automate the complex task of compiling commonsense knowledge bases.

Amao is a cognitive agent framework that tackles the invention of predicates with a different strategy as compared to recent advances in Inductive Logic Programming (ILP) approaches like Meta-Intepretive Learning (MIL) technique. It uses a Neural Multi-Space (NeMuS) graph structure to anti-unify atoms from the Herbrand base, which passes in the inductive momentum check. Inductive Clause Learning (ICL), as it is called, is extended here by using the weights of logical components, already present in NeMuS, to support inductive learning by expanding clause candidates with anti-unified atoms. An efficient invention mechanism is achieved, including the learning of recursive hypotheses, while restricting the shape of the hypothesis by adding bias definitions or idiosyncrasies of the language.