Details
Originalsprache | Englisch |
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Titel des Sammelwerks | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Herausgeber/-innen | Ngoc Thanh Nguyen, Ryszard Kowalczyk, Jaap van den Herik, Ana Paula Rocha, Joaquim Filipe |
Herausgeber (Verlag) | Springer Verlag |
Seiten | 193-220 |
Seitenumfang | 28 |
ISBN (elektronisch) | 9783319783017 |
ISBN (Print) | 9783319783000 |
Publikationsstatus | Veröffentlicht - 2018 |
Publikationsreihe
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Band | 10780 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (elektronisch) | 1611-3349 |
Abstract
Self-adaptation and self-organisation (SASO) are increasingly used in information and communication technology to master complexity and keep the administrative effort at an acceptable level. However, using SASO mechanisms is not an end in itself – the primary goal is typically to allow for a higher autonomy of systems in order to react appropriately to disturbances and dynamics in the environmental conditions. We refer to this goal as achieving “robustness”. During design-time, engineers have different possibilities to develop SASO mechanisms for an underlying control problem. When deciding which path to follow, an analysis of the inherent robustness of possible solutions is necessary. In this article, we present a novel quantification method for robustness that provides the basis to compare different control strategies in similar conditions.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Hrsg. / Ngoc Thanh Nguyen; Ryszard Kowalczyk; Jaap van den Herik; Ana Paula Rocha; Joaquim Filipe. Springer Verlag, 2018. S. 193-220 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10780 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Comparing the Effects of Disturbances in Self-adaptive Systems - A Generalised Approach for the Quantification of Robustness
T2 - A generalised approach for the quantification of robustness
AU - Tomforde, Sven
AU - Kantert, Jan
AU - Müller-Schloer, Christian
AU - Bödelt, Sebastian
AU - Sick, Bernhard
N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Self-adaptation and self-organisation (SASO) are increasingly used in information and communication technology to master complexity and keep the administrative effort at an acceptable level. However, using SASO mechanisms is not an end in itself – the primary goal is typically to allow for a higher autonomy of systems in order to react appropriately to disturbances and dynamics in the environmental conditions. We refer to this goal as achieving “robustness”. During design-time, engineers have different possibilities to develop SASO mechanisms for an underlying control problem. When deciding which path to follow, an analysis of the inherent robustness of possible solutions is necessary. In this article, we present a novel quantification method for robustness that provides the basis to compare different control strategies in similar conditions.
AB - Self-adaptation and self-organisation (SASO) are increasingly used in information and communication technology to master complexity and keep the administrative effort at an acceptable level. However, using SASO mechanisms is not an end in itself – the primary goal is typically to allow for a higher autonomy of systems in order to react appropriately to disturbances and dynamics in the environmental conditions. We refer to this goal as achieving “robustness”. During design-time, engineers have different possibilities to develop SASO mechanisms for an underlying control problem. When deciding which path to follow, an analysis of the inherent robustness of possible solutions is necessary. In this article, we present a novel quantification method for robustness that provides the basis to compare different control strategies in similar conditions.
UR - http://www.scopus.com/inward/record.url?scp=85046352793&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-78301-7_9
DO - 10.1007/978-3-319-78301-7_9
M3 - Contribution to book/anthology
AN - SCOPUS:85046352793
SN - 9783319783000
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 193
EP - 220
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Nguyen, Ngoc Thanh
A2 - Kowalczyk, Ryszard
A2 - van den Herik, Jaap
A2 - Rocha, Ana Paula
A2 - Filipe, Joaquim
PB - Springer Verlag
ER -