A connectionist inference mechanism for a natural language understanding system

We have developed the Connectionist Inference Mechanism (CIM) to serve as a connectionist alternative to the symbolic inference module of P. Buchheit's (1991) Informational Network For A Natural Thinking (INFANT) System. CIM consists of several modules working together, including memory, neural networks, and a binding set. Its main task is to perform inference generation with the capability of variable binding. CIM is essentially a hybrid cognitive model that combines the advantages of a symbolic approach, local representation, and parallel distributed processing. It can be shown that each approach complements and supports the others.