First-order Cascade ARTMAP

First-order theory refinement using neural networks is still an open problem. Towards a solution to this problem, we define a First-Order extension of the Cascade ARTMAP (FOCA) system, using Inductive Logic Programming techniques. To present such a first-order extension of Cascade ARTMAP, we: a) modify the network structure to handle first-order objects; b) define firstorder versions of the main functions that guide all Cascade ARTMAP dynamics, the choice and match functions; c) define a first-order version of the propositional learning algorithm, that approximates Plotkin’s least general generalization (lgg). Results show that our initial goal, learning logic programs using neural networks, has been achieved.