A Caltech Library Service

An Information Theoretic Approach to Rule-Based Connectionist Expert Systems

Goodman, Rodney M. and Miller, John W. and Smyth, Padhraic (1989) An Information Theoretic Approach to Rule-Based Connectionist Expert Systems. In: Advances in Neural Information Processing Systems 1 (NIPS 1988). Advances in Neural Information Processing Systems. No.1. Morgan Kaufmann , San Mateo, CA, pp. 256-263. ISBN 1-558-60015-9.

[img] PDF - Published Version
See Usage Policy.


Use this Persistent URL to link to this item:


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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
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.
Funding AgencyGrant Number
Aerojet GeneralUNSPECIFIED
Series Name:Advances in Neural Information Processing Systems
Issue or Number:1
Record Number:CaltechAUTHORS:20160107-155547718
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:63469
Deposited On:14 Jan 2016 23:46
Last Modified:03 Oct 2019 09:28

Repository Staff Only: item control page