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A framework for validation of rule-based systems.

R Knauf1, A J Gonzalez, T Abel

  • 1Fac. of Comput. Sci. & Autom., Tech. Univ. of Ilmenau.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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This study introduces a five-step methodology for validating rule-based expert systems. It uses a minimal test input set and a Turing Test-like approach to compare system and human expert responses, ensuring system validity.

Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Rule-based expert systems require robust validation methods.
  • Ensuring the accuracy and reliability of expert systems is crucial for their practical application.

Purpose of the Study:

  • To present a comprehensive methodology for validating rule-based expert systems.
  • To develop a practical approach for testing expert system performance against human experts.

Main Methods:

  • A five-step validation process incorporating minimal test input generation and a Turing Test-like evaluation.
  • Expert panels anonymously evaluate system and human expert responses to identify discrepancies and assess validity.
  • Criteria for minimizing test inputs consider user-defined and domain-specific factors.

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Main Results:

  • Demonstrated a technique for creating a practical, minimal set of test inputs for expert systems.
  • Showcased a Turing Test-like evaluation to compare expert system outputs with human expert judgments.
  • Successfully detected seeded errors in a rule-based system using the proposed validation methodology.

Conclusions:

  • The proposed methodology provides a framework for validating rule-based expert systems effectively.
  • Comparing expert system performance to human experts enhances validity assessment and system refinement.
  • The method aids in understanding expert behavior and improving the rule base of expert systems.