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Student Graduation Projects in the Context of Framework for AI-Based Support of Early Conceptual Phases in Architecture

Viktor Eisenstadt; Klaus-Dieter Althoff; Christoph Langenhan
In: Daniel Trabold; Pascal Welke; Nico Piatkowski (Hrsg.). Lernen, Wissen, Daten, Analysen. GI-Workshop-Tage "Lernen, Wissen, Daten, Analysen" (LWDA-2020), September 9 - August 11, Online, Pages 174-179, CEUR, 2020.


In this paper, current, past, and planned student graduation projects in the context of MetisCBR, the distributed AI framework for intelligent support of the early room configuration process in architectural design, will be presented. During the last years, a number of such projects were initiated to achieve a master’s or bachelor’s degree. All these projects have in common that they intend to extend the currently available functionalities of the framework with new features using the modern AI techniques and trends, such as explainable AI or generative adversarial nets, in order to keep up with the recent AI developments. For each project, a summary of the concept(s), results of the experiments (if any), and the current status (e.g., defended or ongoing) will be presented. The main goal of this paper is to reward the student contributions to the MetisCBR framework by making them visible to the research community

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