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Generic Self-Learning Context Sensitive Solution for Adaptive Manufacturing and Decision Making Systems

Authors:
Dragan Stokic
Sebastian Scholze
Oliver Kotte

Keywords: context sensitivity;self-learning systems; adaptive manufacturing systems; context model; descision making systems; software engineering

Abstract:
The paper investigates applications of context sensitivity to achieve high adaptivity of systems. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a wide scope of systems. The approach is applied for high adaptation and integration of complex, flexible manufacturing systems as well as complex decision making systems, e.g., in software engineering. The proposed solution includes context extractor, adapter and self-learning modules allowing for adapting of the process and/or decision support systems depending on the extracted context. Both context extraction and adapter are continuously learning and improving their performance. Service Oriented Architecture (SOA) principles are used to implement these modules. The generic solution and specific applications in various manufacturing environments and decision making processes in software engineering are presented. The paper is one of first attempts to develop holistic context sensitive solution applicable to various systems.

Pages: 73 to 78

Copyright: Copyright (c) IARIA, 2014

Publication date: February 23, 2014

Published in: conference

ISSN: 2308-4243

ISBN: 978-1-61208-319-3

Location: Nice, France

Dates: from February 23, 2014 to February 27, 2014