Generating plausible diagnostic hypotheses with self-processing causal networks

Abstract

A recently proposed connectionist methodology for diagnostic problem-solving is critically examined for its ability to construct problem solutions. A sizeable causal network (56 manifestation nodes, 26 disorder nodes, 384 causal links) served as the basis of experimental simulations. Initial results were discouraging, with less than two-thirds of simulations leading to stable solution states (equilibria). Examination of these simulation results identified a critical period during simulations, and analysis of the connectionist model's activation rule during this period led to an understanding of the model's non-stable oscillatory behavior. Slower decrease in the model's control parameter during the critical period resulted in all simulations reaching a stable equilibrium with plausible problem solutions. As a consequence of this work, it is possible to determine more rationally a schedule for control parameter variation during problem solving, and the way is now open for real-world experimental assessment of this problem-solving method.

Keywords:

• diagnostic problem-solving
• causal models
• connectionist models
• self-processing networks
• nonlinear optimization
• simulated annealing

Additional information

Notes on contributors

JONATHAN WALD

Currently with the Department of Psychiatry, Darmouth Medical School, Hanover, NH.