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Development of Bayesian Diagnostic Models Using Troubleshooting Flow Diagrams

Development of Bayesian Diagnostic Models Using Troubleshooting Flow Diagrams. K. Wojtek Przytula: HRL Laboratories & Don Thompson: Pepperdine University Malibu, California. The Troubleshooting Problem. Given • a malfunctioning system • initial observations

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Development of Bayesian Diagnostic Models Using Troubleshooting Flow Diagrams

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  1. Development of Bayesian Diagnostic Models Using Troubleshooting Flow Diagrams K. Wojtek Przytula: HRL Laboratories & Don Thompson: Pepperdine University Malibu, California

  2. The Troubleshooting Problem Given • a malfunctioning system • initial observations (symptoms, error messages in archive) Derive • a sequence of tests to provide more observations • a diagnosis of one or more defects

  3. Sample System Test Initial Observations

  4. Troubleshooting with Software Assistants • Quantitative measures • Flexible assistance (not restrictive in recommendations) • Use of all available initial evidence • Recommendation of next best test as well as alternatives (or ranked list of tests) • Suggestion of most likely defects (or ranked list of defects) • Suggestion of when to stop troubleshooting, allowing for continuation with next best observation

  5. Three Approaches to Software Assistants • Troubleshooting Flow diagrams (TFD) • Case Based Reasoning (CBR) • Bayesian Networks (BN)

  6. Initial Evidence Troubleshooting Flow Diagram Example T-Test D-Defect

  7. Natural representation of troubleshooting knowledge & process • Single fault diagnosis • Inflexible—no alternative next test • Simple to execute • Hard to create and modify • Provides sequencing of tests and stopping of troubleshooting Troubleshooting Flow Diagram Characteristics

  8. Case Based Example

  9. Given initial evidence, find the best case base • Recommend next best test using separate sequencing algorithm, e.g. decision tree algorithm: ID3 • End of troubleshooting determined by exhausting all tests Application of Case Bases to Troubleshooting

  10. Natural representation of diagnostic knowledge (diagnostic cases) • Single fault diagnosis • Flexible choice of next test at expense of rebuilding decision trees • Hard to create and modify large case bases (e.g. handling of conflicts) Characteristics of Case Based Approach

  11. Structure: Causal Dependencies Between Defects and Tests Bayesian Networks Example Parameters: • Prior Probability Distribution: P(Di) for all i • Conditional Probability Distribution: P(Ti | Dj) for all i,j

  12. Application of Bayesian Networks to Troubleshooting Given initial evidence, provides list of defects ranked by probability Recommends ranked list of next test using separate sequencing algorithm e.g. Value of Information (VOI) algorithm Recommends end of troubleshooting using separate stopping algorithm e.g. heuristic evaluation of ranked lists of defects and tests

  13. Troubleshooting knowledge represented as causal probabilistic model • Multiple and single fault diagnosis • Flexible quantitative choice of next test • Easy to modify and maintain (e.g. learning) • Requires probabilities – obtained by expert estimation or learning from data Characteristics of Bayesian Network Approach

  14. Conversion and Comparison of Three Approaches Algorithm TFD CBR Sequencing Learning Algorithm Algorithm BN Sequencing

  15. Conversion of Flow Diagrams to Case Bases Flow Diagrams to Conversion to Case Bases Trace each path of TFD to create one entry in CB (ordering of tests lost)

  16. Conversion of Flow Diagrams to Bayesian Networks • Represent all defects as states of single defect node • Represent tests as separate children of defect node (sequencing of tests lost) • Probabilities of defects: 1/n. Conditional probabilities: 0, 1, ½

  17. Flow diagrams are natural and popular, but • The user cannot modify test sequence • Time-consuming to create • Different experts = Different diagrams • Impractical for complex systems Conclusions

  18. Seed case base can be created from flow diagrams and then augmented by new cases • For troubleshooting, case base needs to be combined with sequencing algorithms • For sequencing, tests and cases can be weighted or repeated to express test cost or defect frequency • More expressive and flexible than TFD Conclusions – Case Bases

  19. Seed BN can be automatically obtained from flow diagram • BN can be learned from case base • For troubleshooting, BN needs to be combined with sequencing algorithm • Encode failure rates explicitly in the model and express uncertainty about impact of defect on test results • BN can be easily converted from single-fault to multiple-fault diagnostic tool • More flexible solution than TFD or CBR Conclusions – Bayesian Networks

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