Overcoming rule-based rigidity and connectionist limitations through massively-parallel case-based reasoning

Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificial intelligence presents major problems as well. A promising way out of this impasse is to build neural net models that accomplish massively parallel case-based reasoning. Case-based reasoning, which has received much attention recently, is essentially the same as analogy-based reasoning, and avoids many of the problems leveled at traditional artificial intelligence. Further problems are avoided by doing many strands of case-based reasoning in parallel, and by implementing the whole system as a neural net. In addition, such a system provides an approach to some aspects of the problems of noise, uncertainty and novelty in reasoning systems. The current neural net system (Conposit), which performs standard rule-based reasoning, is being modified into a massively parallel case-based reasoning version

Encoding techniques for complex information structures in connectionist systems

Two general information encoding techniques called relative position encoding and pattern similarity association are presented. They are claimed to be a convenient basis for the connectionist implementation of complex, short term information processing of the sort needed in common sense reasoning, semantic/pragmatic interpretation of natural language utterances, and other types of high level cognitive processing. The relationships of the techniques to other connectionist information-structuring methods, and also to methods used in computers, are discussed in detail. The rich inter-relationships of these other connectionist and computer methods are also clarified. The particular, simple forms are discussed that the relative position encoding and pattern similarity association techniques take in the author's own connectionist system, called Conposit, in order to clarify some issues and to provide evidence that the techniques are indeed useful in practice