This paper presents a method for training PDP networks that, unlike backpropagation, does not require excessive amounts of training data or massive amounts of training time to generate appropriate generalizations. The method that we present uses general conceptual knowledge about cause-and-effect relationships within a single training instance to constrain the number of possible generalizations. W e describe how this approach has been previously implemented in rule based systems and we present a method for implementing the rules within the framework of Parallel Distributed Semantic (PDS) Networks, which use multiple PDP networks structured in the form of a semantic network. Integrating rules about causality with backprop in PDS Networks retains the advantages of PDP , while avoiding the problems of enormous numbers of training instances and excessive amounts of training time.

The traditional approach to dynamic inferencing is to represent knowledge in a symbolic hierarchy, find the most specific information in the hierarchy that relates to the input, and apply the attached inferences. This approach provides for inheritance and parallel retrieval but at the expense of very complex learning and access mechanisms. Parallel Distributed Processing (PDP) systems have recently emerged as an alternative. PDP systems use a very simple processing mechanism, but can only eiccess high-level knowledge sequentially and require an enormous amount of training time. This paper presents Parallel Distributed Semantic (PDS) Networks, an approeich that integrates the best features of symbolic and PDP systems by storing the content of symbolic hierarchies in ensembles of P D P networks, connecting the networks in the manner of a semantic network, and using Propagation Filters to determine how information is passed between networks. Simulation results are presented which indicate that P D S Networks and Propagation Filters are able to perform pattern completion from partial input, generate dynamic inferences, and propagate role bindings.

This paper discusses the problem of selecting the correct knowledge structures in parsing natural language texts which are conceptually and structurally ambiguous and require dynamic reinterpretation. An approach to this problem is presented which represnets all knowledge structures in a uniform manner and which uses a constrained marker passing mechanism augmented with elements of connectionist models. This approach is shown to have the advantage of completely integrating all parsing processes, while maintatining a simple, domain-independedt processing mechanism