Abstract
Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to be adapted or deleted in order to restore consistency. A diagnosis algorithm’s performance can be evaluated in terms of time to find a diagnosis (runtime) and diagnosis quality. Runtime efficiency of diagnosis is especially crucial in real-time scenarios such as production scheduling, robot control, and communication networks. However, there is a trade off between diagnosis quality and the runtime efficiency of diagnostic reasoning. In this paper, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach for constraint ordering in direct diagnosis. We show that our approach improves the runtime performance and diagnosis quality at the same time.
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Using the latent factor k=100 and the number of iterations = 1000.
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Acknowledgments
The work presented in this paper has been conducted within the scope of the Horizon 2020 projects OpenReq (Grant Nr. 732463) and AGILE (Grant Nr. 688088).
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Erdeniz, S.P., Felfernig, A., Atas, M. (2019). Learned Constraint Ordering for Consistency Based Direct Diagnosis. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_31
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