Abstract
Due to their simple and intuitive manner rules are often used for the implementation of intelligent systems. Besides general methods for the verification and validation of rule systems there exists only little research on the evaluation of their robustness with respect to faulty user inputs or partially incorrect rules. This paper introduces a gray box approach for testing the robustness of rule systems, thus including a preceding analysis of the utilized inputs and the application of background knowledge. The practicability of the approach is demonstrated by a case study.
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References
Ayel, M., Laurent, J.-P.: Validation, Verification and Test of Knowledge-Based Systems. Wiley, Chichester (1991)
Preece, A., Shinghal, R.: Foundation and Application of Knowledge Base Verification. International Journal of Intelligent Systems 9, 683–702 (1994)
Knauf, R.: Validating Rule-Based Systems: A Complete Methodology. Shaker, Aachen (2000)
Horrocks, I., et al.: OWL Rules: A Proposal and Prototype Implementation. Journal of Web Semantics 3(1), 23–40 (2005), http://www.cs.man.ac.uk/~horrocks/Publications/download/2005/HPBT05.pdf
van Harmelen, F., Groot, P., ten Teije, A.: Torture Tests: A Quantitative Analysis for the Robustness of Knowledge-Based Systems. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNCS (LNAI), vol. 1937, pp. 403–418. Springer, Heidelberg (2000)
Barr, V.: Applications of Rule-Base Coverage Measures to Expert System Evaluation. Knowledge-Based Systems 12, 27–35 (1999)
Baumeister, J.: Agile Development of Diagnostic Knowledge Systems. IOS Press, Amsterdam (2004)
Atzmueller, M., Baumeister, J., Puppe, F.: Semi-Automatic Learning of Simple Diagnostic Scores utilizing Complexity Measures. AI in Medicine 37(1), 19–30 (2006)
Ernst, R.: Untersuchung verschiedener Problemlösungsmethoden in einem Experten- und Tutorsystem zur makroskopischen Bestimmung krautiger Blütenpflanzen [Analysis of various problem solving methods with an expert and tutoring system for the macroscopic classification of flowers]. Master’s thesis, University Würzburg, Biology department (1996)
Puppe, F.: Knowledge Formalization Patterns. In: Proceedings of PKAW 2000, Sydney, Australia (2000)
Hüttig, M., et al.: A Diagnostic Expert System for Structured Reports, Quality Assessment, and Training of Residents in Sonography. Medizinische Klinik 3, 117–122 (2004)
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Baumeister, J., Bregenzer, J., Puppe, F. (2007). Gray Box Robustness Testing of Rule Systems. In: Freksa, C., Kohlhase, M., Schill, K. (eds) KI 2006: Advances in Artificial Intelligence. KI 2006. Lecture Notes in Computer Science(), vol 4314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69912-5_26
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DOI: https://doi.org/10.1007/978-3-540-69912-5_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69911-8
Online ISBN: 978-3-540-69912-5
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