Neural-Symbolic Learning Systems

Abstract

This chapter introduces the basics of neural-symbolic systems used thoughout the book. A brief bibliographical review is also presented. Neural-symbolic systems have become a very active area of research in the last decade. The integration of neural networks and symbolic knowledge was already receiving considerable attention in the 1990s. For instance, in [250], Towell and Shavlik presented the influential model KBANN (Knowledge-Based Artificial Neural Network), a system for rule insertion, refinement, and extraction from neural networks. They also showed empirically that knowledge-based neural networks, trained using the backpropagation learning algorithm (see Sect. 3.2), provided a very efficient way of learning from examples and background knowledge. They did so by comparing the performance of KBANN with other hybrid, neural, and purely symbolic inductive learning systems (see [159, 189] for a comprehensive description of a number of symbolic inductive learning systems, including inductive logic programming systems).