Generating plausible diagnostic hypotheses with self-processing causal networks

Jonathan Wald, Martin Farach, Malle Tagamets, James A. Reggia

    Research output: Contribution to journalArticlepeer-review


    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 problemsolving method.

    Original languageEnglish (US)
    Pages (from-to)91-112
    Number of pages22
    JournalJournal of Experimental and Theoretical Artificial Intelligence
    Issue number2
    StatePublished - 1989


    • Causal models
    • Connectionist models
    • Diagnostic problem-solving
    • Nonlinear optimization
    • Self-processing networks
    • Simulated annealing

    ASJC Scopus subject areas

    • Software
    • Theoretical Computer Science
    • Artificial Intelligence


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