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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.
Summary
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.
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.
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