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Automatic Programming, Learning and Inference

In another project, we examine basic processes related to automatic programming, learning, and inference theory. Specifically, we are interested in mechanisms that can receive samples of a behavior and then create a general representation of the behavior which accounts for the given samples and all "similar" cases. For example, the system might receive samples of the input-output behavior for a computer program and then generate the program. A number of models have been studied including grammatical inference systems, program generators, signature table systems and others. We emphasize the development of systems that can be shown to learn well-defined classes of behaviors and whose convergence characterizations can be understood. We seek both new algorithms for doing such inference and general theory to explain the phenomena.
Tue Aug 23 12:33:31 EDT 1994