Abstract
In this paper, we propose a method for identifying the adaptation period when a problem occurs in a system in order to reduce the unnecessary adaptation of self-adaptive software. Consequently, the dangerous situation information is defined, the behavior information at the time of problem occurrence is learned, and the adaptive performance is determined by comparing it with the existing similar situations by using the k-nearest neighbors algorithm. By the use of the proposed method, a situation where an unnecessary adaptation process is performed while running the self-adaptive system could be avoided, system load may be reduced, and service quality may be enhanced.
Original language | English |
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Title of host publication | Proceedings of the 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015 |
Editors | David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica, Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Hamid R. Arabnia |
Publisher | CSREA Press |
Pages | 399-400 |
Number of pages | 2 |
ISBN (Electronic) | 1601324073, 9781601324078 |
Publication status | Published - 2019 |
Event | 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015 - Las Vegas, United States Duration: 2015 Jul 27 → 2015 Jul 30 |
Publication series
Name | Proceedings of the 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015 |
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Conference
Conference | 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015 |
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Country/Territory | United States |
City | Las Vegas |
Period | 15/7/27 → 15/7/30 |
Bibliographical note
Funding Information:This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2012M3C4A7033346). Doo-Kwon Baik is corresponding author
Publisher Copyright:
© 2019 ICAI 2015 - WORLDCOMP 2015. All rights reserved.
Keywords
- Machine learning
- Problem recognition
- Self-adaptive software
ASJC Scopus subject areas
- Artificial Intelligence
- Software