Symbol grounding using neural networks

The classical approach to artificial intelligence (i.e. symbol manipulation) and the connectionist approach (artificial neural networks) have been criticized for their inadequacies. The philosopher John Searle's Chinese room thought experiment argued that symbolic systems have no understanding of the meaning contained in their representations. The philosophers Jerry Fodor and Zenon Pylyshyn argued that artificial neural networks could not exhibit certain features of human cognition, such as systematicity and composition of representations. We take the view that both of these problems can be solved by a suitable integration of connectionist and symbolic systems. In this work we investigate methods of using artificial neural networks to produce descriptions in propositional and predicate logic. Artificial neural networks are stuctured such that, upon training, simple features of the network correspond directly to either propositional variables in one case, and objects and predicates in the other. In both cases, the methods were tested on character recognition tasks.