Abstract
Machine learning techniques suggest new approaches to the problems encountered in Systems Engineering. This paper presents a framework for the analysis and verification of a class of rule-based realtime decision making systems. This framework is based on the technique of Explanation-based generalization that is used to generalize rule-based programs in order to support both their reuse, analysis and verification. The latter task, in this class of systems, is in general undecidable and in the case where all the variables are restricted to take values in finite domains it is PSPACE-hard. The most important topic addressed in this work is the reuse of existing components in order to support both the evaluation of the worst-case execution time and the automatic verification of the real-time decision making system. The proposed methodology seems to be quite efficient in practical cases.
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© 1996 Springer-Verlag Berlin Heidelberg
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Boutsinas, B., Papadimitriou, S., Pavlides, G. (1996). Automatic analysis, verification and synthesis of rule-based real-time decision making systems with machine learning assistance. In: Bjørner, D., Broy, M., Pottosin, I.V. (eds) Perspectives of System Informatics. PSI 1996. Lecture Notes in Computer Science, vol 1181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62064-8_13
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DOI: https://doi.org/10.1007/3-540-62064-8_13
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