Studies of Learning and Representation in Distributed       Connectionist Networks
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{'SignBlockName': 'Paul Werbos', 'PO_EMAI': None, 'PO_PHON': None}
Massively parallel connectionist systems having distributed                     representations of knowledge, have two fundamental problems:  developing        faster and more effective learning procedures for connectionist                 networks, and developing techniques by which such networks can handle           complex symbolic knowledge structures in addition to the lower-level            sensory knowledge being studied by groups.  The learning work generally         focuses primarily on variations of the existing so-called Boltzmann and         back-propagation procedures.  In this proposed effort "variable                 plasticity" techniques in which not all of the weights have the same            ability to change during learning.  The learning procedures developed           will be evaluated by application to problems in speech understanding,           low-level image processing, and control of a manipulator.  Preliminary          experiments in these areas are described in the main proposal.                                                                                                  The work on symbolic representations will focus on language                     understanding and commonsense reasoning, specifically:  increasing the          subtlety and richness of distributed symbolic representations, combining        multiple sources of syntactic and semantic constraint via parallel              relaxation, investigating problems in matching and complex inference,           using learning to adjust the behavior of an adaptive symbol processor           problems in artificial intelligence and cognitive psychology.                   systems.                                                                                                                                                        The proposed work is cross-disciplinary in nature, applying techniques          from mathematics, computer science and the new field of connectionism to        problems in artificial intelligence, cognitive psychology, and
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