Agent collaboration in a multi-agent-system for analysis and optimization of mechanical engineering parts

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Gembarski, P.C.: Agent collaboration in a multi-agent-system for analysis and optimization of mechanical engineering parts. In: Cristani, M.; Toro, C.; Zanni-Merk, C.; Howlett, R.J.; Jain, L.C. (Eds.): Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020. Amsterdam [u.a.] : Elsevier, 2020 (Procedia computer science ; 176), S. 592-601. DOI: https://doi.org/10.1016/j.procs.2020.08.061

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/10736

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Sum total of downloads: 175




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Abstract: 
In mechanical engineering, designers have to review a designed artefact iteratively with different domain experts, e.g. from manufacturing, to avoid later changes and find a robust, optimized design. To support the designer, knowledge-based engineering offers a set of approaches and techniques that formalize and implement engineering knowledge into generic product models or decision support systems. An implementation which satisfies especially the concurrent nature of today's design processes and allow for multi-objective decision-making is multi-agent systems. Such systems consist of entities that are capable of autonomous action, interact intelligently with their environment, communicate and collaborate. In this paper, such a multi-agent system is discussed as extension for a computer-aided design software where the agents take the role of domain experts, like e.g. manufacturing technologists and make suggestions for the optimization of the design of mechanical engineering parts. A focal point is set on the collaboration concept of the single agents. Therefore, the paper proposes the use of an action-item-list as central information and knowledge sharing platform. © 2020 The Authors. Published by Elsevier B.V.
License of this version: CC BY-NC-ND 4.0 Unported
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Maschinenbau

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 106 60.57%
2 image of flag of United States United States 28 16.00%
3 image of flag of China China 10 5.71%
4 image of flag of No geo information available No geo information available 5 2.86%
5 image of flag of Russian Federation Russian Federation 5 2.86%
6 image of flag of Finland Finland 4 2.29%
7 image of flag of Vietnam Vietnam 3 1.71%
8 image of flag of Ukraine Ukraine 2 1.14%
9 image of flag of Czech Republic Czech Republic 2 1.14%
10 image of flag of Ireland Ireland 1 0.57%
    other countries 9 5.14%

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