Computational Techniques for Learning and Reusing Design Procedures

学习和重用设计过程的计算技术

• 批准号: 0200262
• 负责人: Thomas Stahovich
• 金额: $ 30万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0200262&HistoricalAwards=false
• 关键词: Computational; Techniques; Learning; Reusing; Design

The objective of this research project is to create techniques for capturing and reusing design procedures for parametric design. The proposed techniques will be implemented as a transparent software layer that will be placed over existing design tools, transforming them into trainable design automation tools. The designer will train these tools by solving a few sample design problems. Once trained, the tools will be capable of solving similar problems with a minimum of human intervention. The proposed approach will use decision tree learning algorithms to learn which attributes of the design best indicate which parameter the designer will change at any given point in the design procedure. Background knowledge will be used to improve the learning accuracy and to generalize the learned procedures. Neural networks will be used to learn representations for the implicit design constraints.If successful, this work will help to preserve design knowledge in a reusable form. A tremendous amount of useful information is generated during the process of designing, but often only the final result is recorded, in the form of detailed drawings. When design information is lost, future efforts to maintain the design may result in unexpected failures. Similarly, future designers may have to reinvent what was once known, or they may repeat past mistakes. The proposed work directly addresses these issues by creating techniques for capturing the procedures by which design problems are solved. Solution procedures are the distillation of the designer's understanding of a problem, thus preserving them provides a valuable resource for others who must solve similar problems.

这个研究项目的目标是创建技术捕获和重用的设计过程参数化设计。 所提出的技术将作为一个透明的软件层来实施，该软件层将置于现有的设计工具之上，将它们转换为可培训的设计自动化工具。 设计师将通过解决一些示例设计问题来训练这些工具。 一旦经过训练，这些工具将能够在最少的人为干预下解决类似的问题。 所提出的方法将使用决策树学习算法来学习设计的哪些属性最好地指示设计者将在设计过程中的任何给定点改变哪个参数。 背景知识将被用来提高学习的准确性，并概括学习过程。 神经网络将用于学习隐含设计约束的表示。如果成功，这项工作将有助于以可重用的形式保存设计知识。 在设计过程中会产生大量有用的信息，但通常只以详细图纸的形式记录最终结果。 当设计信息丢失时，将来维护设计的努力可能会导致意外的失败。 同样，未来的设计师可能不得不重新发明曾经已知的东西，或者他们可能会重复过去的错误。 建议的工作直接解决这些问题，通过创建技术来捕获的设计问题解决的程序。 解决方案程序是设计师对问题的理解的升华，因此保存它们为必须解决类似问题的其他人提供了宝贵的资源。