Integrating Symbolic Processing and Neural Networks

Abstract

The apparent dichotomy between symbolic AI processing and distributed neural processing cannot be absolute, since neural networks that capture essential features of human intelligence will also model some of the symbolic processes of which humans are capable. Indeed, a primary goal of biological neural network research is to design systems that can self-organize intelligent symbolic processing capabilities. One such system is the ARTMAP family of neural networks [l], [a]. Most if not all of the purported dichotomies between traditional artificial intelligence and neural network research dissolve within these systems. Although ARTMAP systems are neural networks, they are also a type of selforganizing production system capable of hypothesis testing and memory search. They embody continuous and discrete, .parallel and serial, and distributed and localized properties. Their symbols are compressed, often digital representations, yet they are formed and stabilized through a process of resonant binding that is distributed across the system. They are used to explain and predict data on both the psychological and the neurobiological levels, yet their unique combinations of computational properties are also rapidly finding their way into technology. They are capable of autonomously discovering rules about the environments to which they adapt, yet these rules are emergent properties of network dynamics rather than formal algorithmic statements. On the other hand, these emergent rules can be rewritten as algorithmic if-then rules. This synthesis has become possible because such systems embody genuinely new computational principles. These are not the princi