Building and improving design systems: A machine learning approach

This thesis addresses the issue of building design systems that acquire knowledge and improve their performance by using machine learning techniques. Until recently, no serious attempts have been made at assisting in the knowledge acquisition for the complete design process by machine learning techniques. Previous attempts at identifying machine learning techniques for design knowledge acquisition have not been successful, mainly because they have tried to support a complete design process with a single learning method. This thesis abstracts a prescription for the preliminary design of design systems that learn, from the experience of building one instance called Bridger. The prescription, called M$\sp2$LTD (Mapping Machine Learning To Design) is based on the following steps: (1) analysis of the design process and its decomposition into a collection of smaller tasks; (2) identification of the representation of design objects used in each of the tasks described in Step (1) for the particular domain of interest, and of the strategies each task uses; (3) selection of closely related machine learning paradigms, also called generic learning tasks, that have the characteristics identified in Step (2); and (4) use of additional domain characteristics to select particular machine learning programs, from the collection available in each paradigm found in Step (3), that can acquire the knowledge in the right representation and support the strategies employed. Rarely will an existing machine learning program do the task as specified; but a close match reduces the effort, and M$\sp2$LTD focuses that effort on the important modifications required. Bridger, the system that demonstrates M$\sp2$LTD in the domain of the preliminary design of cable-stayed bridges, is built around two machine learning programs: COBWEB and Protos. COBWEB has been extended significantly along many dimensions to allow it to acquire and use synthesis knowledge. Protos has been modified slightly to allow it to acquire and use redesign knowledge. Bridger's development is described and evaluated in light of M$\sp2$LTD. Two other uses of M$\sp2$LTD in the preliminary design of two design systems in other domains (i.e., ship design and the design of finite-element models) are briefly described; these further illustrate the potential of the approach.