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 problemsolving method.
Original language | English (US) |
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Pages (from-to) | 91-112 |
Number of pages | 22 |
Journal | Journal of Experimental and Theoretical Artificial Intelligence |
Volume | 1 |
Issue number | 2 |
DOIs | |
State | Published - 1989 |
Keywords
- Causal models
- Connectionist models
- Diagnostic problem-solving
- Nonlinear optimization
- Self-processing networks
- Simulated annealing
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Artificial Intelligence