Connectionist approach to diagnostic problem solving using causal networks

In general diagnostic problem solving using causal networks, one is often interested in identifying the most plausible explanation for observed manifestations. Although a number of sequential AI search methods have been applied to this task, these methods suffer from potential combinatorial explosion. It thus seems reasonable to explore whether connectionist modeling methods can overcome this difficulty through the use of extensive explicit parallel processing. A number of studies have recently implemented diagnostic systems using connectionist models, but these are based on a distributed representation and face significant limitations that are summarized in this paper. Accordingly, we have developed a connectionist model of diagnostic inference using a local representation to address these limitations. This model controls spreading activation in a causal network that has rapidly varying connection strengths. It generates diagnostic hypotheses based on the same causal network that would be used by a traditional AI search algorithm. Experimental results are presented that demonstrate that this connectionist model can closely approximate the best diagnostic hypotheses with some causal networks, even when multiple disorders are present simultaneously.