Abstract
There have been a number of approaches to employing neural networks (NNs) in self-adaptive systems; in many cases, generic NNs/deep learning are utilized for this purpose. When this approach is to be applied to improve an adaptation process initially driven by logical adaptation rules, the problem is that (1) these rules represent a significant and tested body of domain knowledge, which may be lost if they are replaced by an NN, and (2) the learning process is inherently demanding given the black-box nature and the number of weights in generic NNs to be trained. In this paper, we introduce the rule-specific Neural Network (rsNN) method that makes it possible to transform the guard of an adaptation rule into an rsNN, the composition of which is driven by the structure of the logical predicates in the guard. Our experiments confirmed that the black box effect is eliminated, the number of weights is significantly reduced, and much faster learning is achieved while the accuracy is preserved.
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Notes
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https://www.tensorflow.org/ (version 2.4).
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We divide the data only to the training and testing set (testing set holds \(10\%\) of data). We do not need a validation set since we do not perform any hyper-parameter training.
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Acknowledgment
This work has been funded by the DFG (German Research Foundation) - project number 432576552, HE8596/1-1 (FluidTrust), supported by the Czech Science Foundation project 20-24814J, partially supported by Charles University institutional funding SVV 260588 and the KASTEL institutional funding, and partially supported by the Charles University Grant Agency project 408622.
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Bureš, T. et al. (2022). Attuning Adaptation Rules via a Rule-Specific Neural Network. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISoLA 2022. Lecture Notes in Computer Science, vol 13703. Springer, Cham. https://doi.org/10.1007/978-3-031-19759-8_14
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