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@InProceedings{SAML21,
AUTHOR = {Casimiro, Maria and Romano, Paolo and Garlan, David and Moreno, Gabriel A. and Kang, Eunsuk and Klein, Mark},
TITLE = {Self-Adaptation for Machine Learning Based Systems},
YEAR = {2021},
MONTH = {14 September},
BOOKTITLE = {Proceedings of the 1st International Workshop on Software Architecture and Machine Learning (SAML)},
SERIES = {LNCS},
ADDRESS = {Virtual, (Originally V\"axjö, Sweden)},
PUBLISHER = {Springer},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/SAML2021-paper6SAML21.pdf},
ABSTRACT = {Today’s world is witnessing a shift from human-written software to machine-learned software, with the rise of systems
that rely on machine learning. These systems typically operate in non-static environments, which are prone to unexpected
changes, as is the case of self-driving cars and enterprise systems. In this context, machine-learned software can misbehave.
Thus, it is paramount that these systems are capable of detecting problems with their machined-learned components and
adapt themselves to maintain desired qualities. For instance, a fraud detection system that cannot adapt its machine-learned
model to efficiently cope with emerging fraud patterns or changes in the volume of transactions is subject to losses of millions
of dollars. In this paper, we take a first step towards the development of a framework aimed to self-adapt systems that rely
on machine-learned components. We describe: (i) a set of causes of machine-learned component misbehavior and a set of
adaptation tactics inspired by the literature on machine learning, motivating them with the aid of a running example; (ii) the
required changes to the MAPE-K loop, a popular control loop for self-adaptive systems; and (iii) the challenges associated
with developing this framework. We conclude the paper with a set of research questions to guide future work.},
KEYWORDS = {Machine Learning, Self-adaptation}
}
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