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.
