metis II - Artificial intelligence methods for auto-completion of designs based on semantic building information (BIM) for supporting architects in early design phases.

metis II - 基于语义建筑信息 (BIM) 自动完成设计的人工智能方法，用于在早期设计阶段为建筑师提供支持。

• 批准号: 419390235
• 负责人: Professor Dr. Andreas Dengel
• 金额: --
• 依托单位: Bereich Intelligente Informationssysteme (IIS)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/419390235?language=en
• 关键词: metis; II; Artificial; intelligence; methods

The aim of the "metis II" project is to develop methods for auto-completion of building designs. Using artificial intelligence approaches, (partial) information is extracted from reference designs and proposed to the architect as additions to his own design. Thus, methods are developed to apply useful information from a semantic building model (BIM) in the design context. Methods are examined to suggest e.g. kitchens, corridors or bathrooms and e.g. their location to a given formalized spatial configuration of e.g. a living room and a bedroom. The methods to be developed must be able to recognize both the context of the building design and the user-specific context. For this purpose, methods of artificial intelligence (AI), especially case-based reasoning (CBR) and artificial neural networks (KNN), are applied, expanded and developed. The explainability of the system to be developed (XAI - Explainable AI) is a further focus of the project, since the automatically generated solution parts as well as the learned knowledge must be explained to the user - in addition to the usual search results.For the integration of (partial) information from digital semantic building information models (BIM) as semantic and topological design specifications (spatial arrangement), the steps of the CBR cycle are fully integrated into the design process and new methods for the domain-specific adaptation of CBR knowledge containers (case basis, similarity measure, vocabulary and adaptation knowledge) are developed. The current state of (CBR)-research results in acquisition and administrative deficits: A special challenge of CBR, especially of retrieve and retain steps, is the size and quality of the case base, since the largest and most high-quality database possible is required. However, the data must be acquired, processed and at the same time the quality of the cases must be ensured. In order to increase the quality of the database, deep-learning methods for targeted "forgetting" are examined in the project "metis II".The project "metis II" is based on the results of metis I ("metis - Knowledge-based search and query methods for the development of semantic information models (BIM) for use in early design phases" funded by the DFG from 2013-2017. In "metis I", approaches for drawing a retrieval for semantic building models (BIM) were investigated and methods were developed to process the information. The concept of semantic building fingerprints used for this has proven to be robust enough and the methodological approaches have been confirmed.

“metis II”项目的目标是开发自动完成建筑设计的方法。使用人工智能方法，从参考设计中提取（部分）信息，并将其作为建筑师自己设计的补充提供给建筑师。因此，开发了一些方法来在设计环境中应用来自语义建筑模型 (BIM) 的有用信息。检查方法以提出建议，例如厨房、走廊或浴室等它们的位置到给定的形式化空间配置，例如一间客厅和一间卧室。要开发的方法必须能够识别建筑设计的背景和用户特定的背景。为此，应用、扩展和发展了人工智能（AI）方法，特别是基于案例的推理（CBR）和人工神经网络（KNN）。待开发系统的可解释性（XAI - 可解释的人工智能）是该项目的另一个重点，因为除了通常的搜索结果之外，还必须向用户解释自动生成的解决方案部分以及学到的知识。将数字语义建筑信息模型 (BIM) 的（部分）信息集成为语义和拓扑设计规范（空间布置），CBR 循环的步骤完全集成到设计过程中，以及针对特定领域适应的新方法CBR知识容器（案例基础、相似性测量、词汇和适应知识）得到发展。 (CBR) 研究结果在获取和管理缺陷方面的现状：CBR（尤其是检索和保留步骤）的一个特殊挑战是案例库的大小和质量，因为最大和最高质量的数据库可能是必需的。然而，必须获取、处理数据，同时必须确保案例的质量。为了提高数据库的质量，“metis II”项目研究了针对目标“遗忘”的深度学习方法。“metis II”项目基于 metis I（“metis - 基于知识的知识”）的结果。用于开发早期设计阶段使用的语义信息模型 (BIM) 的搜索和查询方法”由 DFG 自 2013 年至 2017 年资助。在“metis I”中，研究了绘制语义建筑模型 (BIM) 检索的方法并开发了处理信息的方法。用于此的语义构建指纹的概念已被证明足够强大，并且方法已得到证实。