Machine Learning in Engineering Design: A Constructive Induction Approach

这项跨学科的研究旨在确定 最近开发的机器学习的可行性和实用性 获得和/或创建概念设计的方法 知识 具体来说，这项研究调查，提高和 适应于设计问题的新方法的建设性归纳法 能够自动发明新的面向问题的 与传统的机器学习相比， 只使用用户预先给定的属性的方法。 研究了两种新的方法--数据驱动的构造性DCI方法 假设驱动的建构性归纳（hypothesis driven constructive induction） 本研究的第一部分研究，适应， 实验测试的学习问题的方法 高层建筑风撑概念设计决策规则 建筑 主要的训练数据是一组最佳的 通过设计和优化产生的（最小重量）设计 计算机系统. 第二部分，方法论经验 将用于建议和实施改进， 方法，并将它们协同整合到一个单一的 系统，专门面向概念设计的问题。 这一经验也将用于开发一种方法， 在设计中应用建构归纳学习系统 知识获取 由此产生的系统和方法将是 在工业环境中测试。 这项研究有可能 通过开发新的 为设计者提供强大的工具，以及做出贡献 机器学习领域的研究， 现实世界中的实际和重要问题。

This interdisciplinary research aims at determining the feasibility and practicality of recently developed machine learning methods to the acquisition and/or creation of conceptual design knowledge. Specifically, this research investigates, enhances and adapts to the design problems new methods of constructive induction that are capable of automatically inventing new problem-oriented attributes, in contrast to the conventional machine learning methods that use only the attributes given in advance by the user. Two novel approaches are investigated-DCI (data-driven constructive induction) and HCI (hypothesis-driven constructive induction). The first part of this research studies, adapts, and experimentally tests the methods on the problems of learning decision rules for conceptual design of wind bracing in tall buildings. The primary training data is a collection of optimal (minimum weight) designs produced by a design and optimization computer system. In the second part, the methodological experience gained will be used to suggest and implement improvement to the methods, and to synergistically integrate them into a single system, specifically oriented toward problems of conceptual design. This experience will be also utilized to develop a method for applying constructive induction-based learning systems in design knowledge acquisition. The resulting system and method will be tested in an industrial environment. The research has a potential to significantly improve conceptual design by developing a new powerful tool for the designers, as well as to make contributions to the field of machine learning by conducting the study in the context of practical and important real-world problems.