BIB-VERSION:: CS-TR-v2.0
          ID:: STAN//CS-TR-95-1553
       ENTRY:: July 19, 1995
ORGANIZATION:: Stanford University, Department of Computer Science
       TITLE:: Modeling techniques and algorithms for probabilistic
               model-based diagnosis and repair
        TYPE:: Thesis
        TYPE:: Technical Report
      AUTHOR:: Srinivas, Sampath
        DATE:: July 1995
       PAGES:: 125
    ABSTRACT:: Model-based diagnosis centers on the use of a behavioral
               model of a system to infer diagnoses of anomalous behavior.
               For model-based diagnosis techniques to become practical,
               some serious problems in the modeling of uncertainty and in
               the tractability of uncertainty management have to be
               addressed. These questions include: How can we tractably
               generate diagnoses in large systems? Where do the prior
               probabilities of component failure come from when modeling a
               system? How do we tractably compute low-cost repair
               strategies? How can we do diagnosis even if only partial
               descriptions of device operation are available? This
               dissertation seeks to bring model-based diagnosis closer to
               being a viable technology by addressing these problems.

               We develop a set of tractable algorithms and modeling
               techniques that address each of the problems introduced
               above. Our approach synthesizes the techniques used in
               model-based diagnosis and techniques from the field of
               Bayesian networks.
       NOTES:: [Adminitrivia V1/Prg/19950719]
         END:: STAN//CS-TR-95-1553