Dynamic decision networks for decision-making in self-adaptive systems a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci...

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Vydáno v:Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems s. 113 - 122
Hlavní autoři: Bencomo, Nelly, Belaggoun, Amel, Issarny, Valerie
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: Piscataway, NJ, USA IEEE Press 20.05.2013
Edice:ACM Conferences
Témata:
Applied computing
Applied computing > Operations research
Applied computing > Operations research > Decision analysis
Computing methodologies
Computing methodologies > Artificial intelligence
Computing methodologies > Artificial intelligence > Knowledge representation and reasoning
Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Probabilistic reasoning
Computing methodologies > Machine learning
Computing methodologies > Machine learning > Learning paradigms
Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning
Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning > Motif discovery
Information systems
Information systems > Information systems applications
Information systems > Information systems applications > Computing platforms
Information systems > Information systems applications > Decision support systems
dynamic decision networks
self-adaptive systems
uncertainty modeling
bayesian networks
ISBN:9781467344012, 146734401X
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Shrnutí:Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and specifically in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, specifically Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision-making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential benefits of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.
ISBN:9781467344012
146734401X
DOI:10.5555/2663546.2663565