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Minimal Cardinality Diagnosis in Problems with Multiple Observations.

Meir Kalech1, Roni Stern1,2, Ester Lazebnik1

  • 1Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer-Sheva 8410501, Israel.

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This study introduces two Boolean satisfiability (SAT) solver approaches for Model-Based Diagnosis (MBD) with multiple observations. These methods address challenges posed by intermittently failing components in complex systems.

Keywords:
behavior modesintermittent faultsmodel-based diagnosismultiple observations

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Systems Engineering

Background:

  • Model-Based Diagnosis (MBD) is a standard technique for identifying system faults using system models.
  • Diagnoses are explanations of which components are faulty, derived from observed abnormal behavior.
  • Handling multiple observations, especially with intermittent faults, presents significant challenges in MBD.

Purpose of the Study:

  • To develop and evaluate SAT-based approaches for Model-Based Diagnosis with multiple observations.
  • To address the complexities introduced by intermittently failing components in diagnostic systems.
  • To compare the efficacy of two distinct SAT-based strategies for MBD.

Main Methods:

  • Formulating the MBD problem with multiple observations as a Boolean satisfiability (SAT) problem.
  • Developing a first approach that compiles the entire problem into a single SAT formula.
  • Developing a second approach that solves each observation independently and then integrates the results.

Main Results:

  • Experimental comparison of the two SAT-based approaches on a standard diagnosis benchmark.
  • Analysis of the advantages and disadvantages of each proposed method.
  • Demonstration of the feasibility of using SAT solvers for complex MBD scenarios.

Conclusions:

  • SAT-based methods offer a promising avenue for solving MBD with multiple observations.
  • The choice between the single-formula and independent-solving approaches depends on specific system characteristics and diagnostic needs.
  • Further research can refine these SAT-based techniques for improved diagnostic accuracy and efficiency.