The Probabilistic Interpretation of Model-Based Diagnosis

Model-based diagnosis is the field of research concerned with the problem of finding faults ms by reasoning with abstract models of the systems. Typically, such models offer a description of the structure of the system in terms of a collection of interacting components. For each of these components it is described how they are expected to behave when functioning normally or abnormally. The model can then be used to determine which combination of components is possibly faulty in the face of observations derived from the actual system. There have been various proposals in literature to incorporate uncertainty into the diagnostic reasoning process about the structure and behaviour of systems, since much of what goes on in a system cannot be observed. This paper proposes a method to decompose the probability distribution in probabilistic model-based diagnosis, partly in terms of the Poisson-binomial probability distribution.

• Type: Conference
• Year: 2009
• Where: ECSQARU
• Authors: Ildikó Flesch, Peter J. F. Lucas
• Keywords: Artificial Intelligence, ECSQARU 2009, Faults Ms, Model-based Diagnosis, Probability Distribution