Efficient Predicate Invention using Shared NeMuS

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.

Authors

• Edjard Mota
• Jacob M. Howe
• Ana Schramm
• Artur S. d'Avila Garcez

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