Theory Refinement in Neural Networks

In this chapter, we present the Connectionist Inductive Learning and Logic Programming System (C-IL²P). C-IL²P is a Neural-Symbolic Learning System based on a feedforward neural network that integrates inductive learning from examples and background knowledge, with deductive learning from Logic Programming. Starting with the background knowledge represented by a propositional logic program, a translation algorithm is applied, generating a neural network that can be trained with examples. The network also computes the stable model (resp. the answer set) of the general (resp. extended) logic program inserted in it as background knowledge, or learned with the examples, thus functioning as a parallel system for Logic Programming.