NDLI: Decision Supporting Approach under Uncertainty for Feature-Oriented Adaptive System

Content Provider	IEEE Xplore Digital Library
Author	Hyo-Cheol Lee Seok-Won Lee
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Eng., Ajou Univ., Suwon, South Korea (Hyo-Cheol Lee; Seok-Won Lee)
Abstract	A self-adaptive system is able to adjust its behavior in response to its perception of the environment. One challenging issue for a self-adaptive system is to deal with its variability. The self-adaptive system should manage their diverse functionalities and behaviors and select one of them depending on the encountered environments. To handle this variability, many adaptive systems adopt the feature model to represent their architecture. A feature model is able to set up all possible system architectures by representing the available alternatives and their relations. However, the relations between features are complex and intertwined with each others. These characteristics cause uncertainty which disturbs the decision making. Uncertainty means the incomplete and insufficient condition to make a decision. Thus, determining an appropriate feature under uncertainty is still a challenging problem. In this paper, we propose a modeling process to support decision making under uncertainty for feature-oriented adaptive systems. By following the proposed process, the engineer is able to extract and design decision support model from the system feature model. In the decision support model, to mitigate uncertainty, we fragmentize the system features into several pieces including only highly relevant features with probability values between intra-and inter-fragments. Through these probabilities, the system analyzes the impact of uncertainty on a decisions and determines an appropriate decision with this analyzed information.
Starting Page	324
Ending Page	329
File Size	342722
Page Count	6
File Format	PDF
ISSN	07303157
e-ISBN	9781467365642
DOI	10.1109/COMPSAC.2015.157
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-01
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Adaptation models Smart grids Decision making Production Meteorology Bayes methods Multi-Entity Bayesian Network Self-Adaptive System Decision Support Model Feature-Model
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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