On Using Analogy to Reconcile Connections and Symbols
How do we gain both the standard advantages of connectionism and those of symbolic systems, without adopting hybrid symbolic/connectionist systems? Fully connectionist systems that support analogy-based reasoning are proposed as an answer, at least in the realm of high-level cognitive processing. This domain includes commonsense reasoning and the semantic/pragmatic aspects of natural language processing. The proposed type of system, purely by being analogy-based, gains forms of graceful degradation, representation completion, similarity-based generalization, learning, rule-emergence and exception-emergence. The system therefore gains advantages commonly associated with connectionism, although the precise forms of the benefits are different. At the same time, through being fully connectionist, the system also gains the traditional connectionist variants of those advantages, as well as gaining further advantages not provided by analogy-based reasoning per se. And, because the system is in part an implementation of a form of symbolic processing, it preserves the flexible handling of complex, temporary structures that are well supported in traditional artificial intelligence and which are essential for high-level cognitive processing. This chapter is in part a reaction against the excessive polarization of the connectionism/symbolicism debate. This polarization is seen as resulting from over-simplified, monolithic views both of what symbolic processing encompasses and of the nature of the benefits that connectionism provides.