Neural network models for rule-based reasoning

Neural network (connectionist) models of rule-based reasoning are investigated, and it is shown that while such models usually carry out reasoning in exactly the same way as symbolic systems, they have more to offer in terms of commonsense reasoning. A connectionist architecture for commonsense reasoning, CONSYDERR, is proposed to account for commonsense reasoning patterns and to remedy the brittleness problem in traditional rule-based systems. A dual representational scheme is devised, utilizing both localist and distributed representations and exploring the synergy resulting from the interaction between the two. CONSYDERR is therefore capable of accounting for many difficult patterns in commonsense reasoning. It is concluded that connectionist models of reasoning are not just implementations of their symbolic counterparts, but better computational models of commonsense reasoning