An Information Theoretic Approach to Rule-Based Connectionist Expert Systems
We discuss in this paper architectures for executing probabilistic rule-bases in a parallel manner, using as a theoretical basis recently introduced information-theoretic models. We will begin by describing our (non-neural) learning algorithm and theory of quantitative rule modelling, followed by a discussion on the exact nature of two particular models. Finally we work through an example of our approach, going from database to rules to inference network, and compare the network's performance with the theoretical limits for specific problems.
Additional Information© 1989 Morgan Kaufmann. This work is supported in part by a grant from Pacific Bell, and by Caltech's program in Advanced Technologies sponsored by Aerojet General, General Motors and TRW. Part of the research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. John Miller is supported by NSF grant no. ENG-8711673.