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
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.
