Integrating probabilistic, taxonomic and causal knowledge in abductive diagnosis

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 inde­ pendence assumptions; taxonomic knowledge allows causation to be modeled at different lev­ els of detail, and allows observations be de­ scribed 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 expla­ nation of a set of observations to be an occur­ rence of a chain of causation events. These cau­ sation events constitute a scenario where all the observations are true. We show that the prob­ abilities 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 exponen­ tial 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 exponen­ tial in the size of the knowledge base.