Towards a Hybrid Model of First-Order Theory Refinement
Nelson A. Hallack, Gerson Zaverucha and Valmir C. Barbosa

Abstract

The representation and learning of a first-order theory using neural networks is still an open problem. We define a propositional theory refinement system which uses min and max as its activation functions, and extend it to the first-order case. In this extension, the basic computational element of the network is a node capable of performing complex symbolic processing. Some issues related to learning in this hybrid model are discussed.