Self-Adaptation for Machine Learning Based Systems
Maria Casimiro, Paolo Romano,
David Garlan,
Gabriel A. Moreno,
Eunsuk Kang and Mark Klein.
In Proceedings of the 1st International Workshop on Software Architecture and Machine Learning (SAML), Springer, Virtual, (Originally Växjö, Sweden), 14 September 2021.
Online links:   Plain Text
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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