For discrete-time probabilistic models there are efficient methods to check whether they satisfy certain properties. If a property is refuted, available techniques can be used to explain the failure in form of a counterexample. However, there are no scalable approaches to repair a model, i.e., to modify it with respect to certain side conditions such that the property is satisfied. In this paper we propose such a method, which avoids expensive computations and is therefore applicable to large models. A prototype implementation is used to demonstrate the applicability and scalability of our technique.
CITATION STYLE
Pathak, S., Ábrahám, E., Jansen, N., Tacchella, A., & Katoen, J. P. (2015). A greedy approach for the efficient repair of stochastic models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9058, pp. 295–309). Springer Verlag. https://doi.org/10.1007/978-3-319-17524-9_21
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