Integrating Probabilistic, Taxonomic and Causal Knowledge in Abductive Diagnosis
This addresses the computational efficiency problem in AI diagnosis systems, though it appears incremental by building on existing abduction methods.
The paper tackles the complexity of abductive diagnosis by integrating probabilistic, causal, and taxonomic knowledge to compute likely explanations as chains of causation events, resulting in an algorithm that is exponential only in the number of observations rather than the knowledge base size.
We propose an abductive diagnosis theory that integrates probabilistic, causal and taxonomic knowledge. Probabilistic knowledge allows us to select the most likely explanation; causal knowledge allows us to make reasonable independence assumptions; taxonomic knowledge allows causation to be modeled at different levels of detail, and allows observations be described in different levels of precision. Unlike most other approaches where a causal explanation is a hypothesis that one or more causative events occurred, we define an explanation of a set of observations to be an occurrence of a chain of causation events. These causation events constitute a scenario where all the observations are true. We show that the probabilities of the scenarios can be computed from the conditional probabilities of the causation events. Abductive reasoning is inherently complex even if only modest expressive power is allowed. However, our abduction algorithm is exponential only in the number of observations to be explained, and is polynomial in the size of the knowledge base. This contrasts with many other abduction procedures that are exponential in the size of the knowledge base.