[Next] [Up] [Previous]
Next: Q-DAG Generation Up: Query DAGs: A Practical Previous: Optimization in Belief-Network Inference

A Diagnosis Example


This section contains a comprehensive example illustrating the application of the Q-DAG framework to diagnostic reasoning.

Figure: A simple belief network for car diagnosis.  

Consider the car troubleshooting example depicted in Figure 14. For this simple case we want to determine the probability distribution for the fault node, given evidence on four sensors: the battery-, alternator-, fuel- and oil-sensors. Each sensor provides information about its corresponding system. The fault node defines five possible faults: normal, clogged-fuel-injector, dead-battery, short-circuit, and broken-fuel-pump.

If we denote the fault variable by F, and sensor variables by tex2html_wrap_inline1712, then we want to build a system that can compute the probability tex2html_wrap_inline1714 for each fault tex2html_wrap1706 and any evidence tex2html_wrap1049. These probabilities represent an unnormalized probability distribution over the fault variable given sensor readings. In a Q-DAG framework, realizing this diagnostic system involves three steps: Q-DAG generation, reduction, and evaluation. The first two steps are accomplished off-line, while the final step is performed on-line. We now discuss each one of the steps in more detail.