  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.
