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The approach takes self-adaptive systems as multi-agent organizations and enables the agent itself to make decisions on self-adaptation by learning at run-time and at different levels. The proposed self-adaptation mechanisms that are based on organization metaphors enable self-adaptation at two layers: fine-grain behavior level and coarse-grain organization level. Corresponding reinforcement learning algorithms on self-adaptation are designed and integrated with the two-layer self-adaptation mechanisms. This paper further details developmental technologies, based on the above approach, in establishing self-adaptive systems, including extended software architecture for self-adaptation, an implementation framework, and a development process. A case study and experiment evaluations are conducted to illustrate the effectiveness of the proposed approach.<\/p>","DOI":"10.4018\/ijats.2014010104","type":"journal-article","created":{"date-parts":[[2014,6,17]],"date-time":"2014-06-17T14:29:07Z","timestamp":1403015347000},"page":"65-85","source":"Crossref","is-referenced-by-count":3,"title":["A Two-Layer Approach to Developing Self-Adaptive Multi-Agent Systems in Open Environment"],"prefix":"10.4018","volume":"6","author":[{"given":"Xinjun","family":"Mao","sequence":"first","affiliation":[{"name":"Science and Technology on Parallel and Distributed Processing Lab, Department of Computer Science and Technology, College of Computer, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Menggao","family":"Dong","sequence":"additional","affiliation":[{"name":"Lab of Science and Technology on Integrated Logistic Support, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Mathematics, Nipissing University, North Bay, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2432","reference":[{"key":"ijats.2014010104-0","unstructured":"Alonso, E., Karcanias, N., & Hessami, A. 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