Concepts in Design Yoram Reich Steven J. Fenves 1. Introduction Design is an ill-structured problem, in which there are often vague criteria for `success', the search space is ill-defined, and resources (e.g., time) are limited. Building expert systems for design, henceforth called design systems, requires relevant domain knowledge and representation schemes that support the solution of this inherently ill-structured problem. However, manual knowledge acquisition has been a bottleneck in creating expert systems in general and design systems more specifically. One approach for alleviating some of the difficulties of knowledge acquisition is the introduction of learning into the development and maintenance stages of expert systems (Reich & Fenves, 1989; Steels & Van de Velde, 1986). Learning can assist in automatically building design systems as well as in understanding a specific design domain. 2 Y. Reich and S. J. Fenves The principal emphasis of this chapter is on the acquisition o...

Machine learning techniques are perceived to have a great potential as means for the acquisition of knowledge; nevertheless, their use in complex engineering domains is still rare. Most machine learning techniques have been studied in the context of knowledge acquisition for well defined tasks, such as classification. Learning for these tasks can be handled by relatively simple algorithms. Complex domains present difficulties that can be approached by combining the strengths of several complementing learning techniques, and overcoming their weaknesses by providing alternative learning strategies. This study presents two perspectives, the macro and the micro, for viewing the issue of multistrategy learning. The macro perspective deals with the decomposition of an overall complex learning task into relatively well-defined learning tasks, and the micro perspective deals with designing multistrategy learning techniques for supporting the acquisition of knowledge for each task. The two pers...

: Design is conceptualized as an ill-structured process that requires diverse knowledge that is hard to acquire. Systematic analysis of design and the knowledge requirements is has in general and in the context of bridge design shows that the knowledge needed can be semi-automatically acquired by using machine learning techniques. Although there are limitations to the approach, preliminary results in the bridge design domain are promising and can potentially transfer to other design domains. 1 INTRODUCTION The process of knowledge acquisition for any type of expert system is time-consuming and tedious. This effort increases when dealing with design domains that are ill-structured by their nature. One approach that promises to alleviate the difficulty of the knowledge acquisition process is the introduction of learning into system development and maintenance stages (Reich and Fenves, 1989b; Shalin et al., 1988; Witten and MacDonald, 1988). For tasks that are relatively well understood ...

Aesthetics plays a major role in real design. To date, aesthetics is mostly ignored in studies dealing with computational design support systems. Probably, the main reason for this omission is that aesthetics is tightly associated with art which is perceived to be beyond the capabilities of computational techniques. The paper outlines a model for the incorporation of aesthetic judgment in design. It shows how important aesthetic criteria that follow the rationalistic and the romanticist movements of aesthetics, can be represented, refined, and used in design. Bridger, a system that assists in the preliminary design of cable-stayed bridges, implements a preliminary version of this model. Several examples of designs generated by Bridger are discussed for demonstrating the scope and potential of the model. The most significant investigations in bridge aesthetics deal primarily with fundamental philosophical and psychological problems or direct aesthetic analysis of existing bridges, prov...

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