Abstract
There have been a number of approaches to employ neural networks in self-adaptive systems; in many cases, generic neural networks and 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 a neural network, and (2) the learning process is inherently demanding given the black-box nature and the number of weights in generic neural networks to be trained. In this paper, we introduce the rule-specific neural network method that makes it possible to transform the guard of an adaptation rule into a rule-specific neural network, 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 whilst the accuracy is preserved. This text is an extended version of the paper presented at the ISOLA 2022 conference (Bureš et al. in Proceedings of ISOLA 2022, Rhodes, Greece, pp. 215–230, 2022).
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Funding
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 260698/2023 and funding from the topic Engineering Secure Systems of the Helmholtz Association (HGF) and by KASTEL Security Research Labs, and partially supported by the Charles University Grant Agency project 408622.
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Bureš, T., Hnětynka, P., Kruliš, M. et al. Generating adaptation rule-specific neural networks. Int J Softw Tools Technol Transfer 25, 733–746 (2023). https://doi.org/10.1007/s10009-023-00725-y
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DOI: https://doi.org/10.1007/s10009-023-00725-y