Supporting model-based diagnosis with explanation-based learning and analogical inferences
This paper introduces two learning approaches used in different phases of a diagnostic system's life cycle. First, an initial knowledge-base is built using an explanation-based learning approach which generates diagnostic rules. A functional model of the object to be diagnosed constitutes the necessary domain knowledge. Later when the system is operational, analogical inferences which utilize taxonomic information continue to improve its diagnostic performance. In this way knowledge which is ‘objectivized’ by the model can be acquired, greatly improving the performance of a pure model-based diagnosis while preserving the advantages of the model-based approach.
Keywordsanalogical inference explanation-based learning machine learning model-based diagnosis
Unable to display preview. Download preview PDF.
- [Carbonell 86]Carbonell, J. G.: Derivational Analogy — A Theory of Reconstructive Problem Solving and Expertise Acquisition. In: Michalski, R. S.; Carbonell, J. G.; Mitchell, T. M. (eds.): Machine Learning — An Artificial Intelligence Approach, Vol. 2. Los Altos, Calif.: Morgan Kaufmann, 1986.Google Scholar
- [Davis 84]
- [DeJong & Mooney 86]DeJong, G.; Mooney, R.: Explanation-Based Learning — An Alternative View. In: Machine Learning 1 (1986) 2.Google Scholar
- [deKleer & Brown 84]
- [deKleer & Williams 87]
- [Mitchell et al. 86]Mitchell, T. M.; Keller, R.; Kedar-Cabelli, S.: Explanation-Based Generalisation — A Unifying View. In: Machine Learning 1 (1986) 1.Google Scholar
- [Spur & Weiß 90]Spur, G.; Weiß, S.: Application of Model-based Diagnosis to Machine Tools. Proc. of the International Workshop of Expert Systems in Engineering, September 24–26, 1990, Vienna. Berlin, Heidelberg, New York: Springer, 1990.Google Scholar
- [Struß 89]Struß, P.: Structuring of Models and Reasoning About Quantities in Qualitative Reasoning. Ph. D. Thesis, University of Kaiserslautern, January 1989.Google Scholar