The use of aggregation in causal simulation
Artificial Intelligence
Artificial Intelligence
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This paper lays the foundation for a diagnostic system that improves its performance by deriving symptom-fault associations from an underlying causal model and then utilizes those relationships to impose further structure upon the "deep" model. A qualitative version of sensitivity analysis is introduced to extract the implicit symptom-fault information from a set of local constraints. Parameter aggregation triggered by this new information then simplifies diagnosis by forming a more abstract causal representation. The resulting diagnostician thus employs both an experiential and a first-principle approach, where in this case "experiences" are compiled directly from first-principles. Key issues include the roles of knowledge compilation and abstraction in refining qualitative models of physical systems.