Explanation-Driven Self-Adaptation Using Model-Agnostic Interpretable Machine Learning

Self-adaptive systems increasingly rely on black-box predictive models (e.g., Neural Networks) to make decisions and steer adaptations. The lack of transparency of these models makes it hard to explain adaptation decisions and their possible effects on the surrounding environment. Furthermore, adapt...

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Veröffentlicht in:	ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems (Online) S. 189 - 199
Hauptverfasser:	Negri, Francesco Renato, Nicolosi, Niccolo, Camilli, Matteo, Mirandola, Raffaela
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 15.04.2024
Schlagworte:	Adaptation models Closed box Costs Explainable Self-adaptation Inter-pretable Machine Learning Machine learning Model-agnostic explanations Neural networks Planning Predictive models
ISSN:	2157-2321
Online-Zugang:	Volltext
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