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”项目的目的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，并向建筑师提出，作为他自己设计的补充。因此，开发了将语义建筑模型（BIM）中的有用信息应用于设计上下文的方法。研究方法建议，例如厨房，走廊或浴室，例如，它们的位置到一个给定的形式化空间配置，例如客厅和卧室。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，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）得到了应用、扩展和发展。待开发系统的可解释性（XAI -可解释的AI）是项目的进一步重点，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习到的知识。为了将来自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（空间安排），将CBR周期的步骤完全集成到设计过程中，并开发了针对CBR知识容器（案例基础、相似性度量、词汇和适应知识）的特定领域适应的新方法。（CBR）的现状-研究导致获取和管理缺陷：CBR的一个特殊挑战，特别是检索和保留步骤，是案例库的大小和质量，因为需要尽可能最大和最高质量的数据库。但是，必须获取和处理数据，同时必须确保案件的质量。为了提高数据库的质量，在“metis II”项目中研究了针对性“遗忘”的深度学习方法。“metis II”项目基于metis I（“metis -用于早期设计阶段的语义信息模型（BIM）开发的基于知识的搜索和查询方法”）的结果，该项目由DFG于2013-2017年资助。在“metis I”中，研究了语义建筑模型（BIM）的绘图检索方法，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是足够健壮的，并且方法方法已得到证实。

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.