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
