Diagnostic Bayesian networks with Fuzzy evidence

Diagnostic Bayesian networks, one of the models supported by the Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE) standard, are an important and commonly used tool for modeling systems for fault isolation. When performing the tests specified by the diagnostic Bayesian network, the test program often maps the raw test measurements to discrete Pass or Fail outcomes. We would like to relax this hard discretization requirement and instead represent degrees of passing and failing. To do this, we propose a method for integrating fuzzy set theory and diagnostic Bayesian networks. Our proposed approach further demonstrates the extensions described in previous work to include gray-scale health information in AI-ESTATE. The previous work demonstrated the use of soft outcomes in AI-ESTATE's Fault Tree Model (FTM); however, no process was given for incorporating the soft outcomes into the other models specified by AI-ESTATE. Here, we describe how to extend the AI-ESTATE Bayesian Network Model (BNM) to incorporate the previously proposed soft outcomes. Because D-matrices and diagnostic logic models can be represented as Bayesian networks, the proposed approach can be adapted to work with AI-ESTATE's D-matrix Inference Model (DIM) and Diagnostic Logic Model (DLM) as well.