An `AI in Design' View of Design
Introduction

The Goals of the Paper

Most researchers in the AI in Design field believe that although design is very complex, with variations in activity depending on the problem, the domain, and on the designers experience, there are common design tasks and knowledge.

AI in Design research is concerned with the use of AI techniques to study design. This is done by making hypotheses about designing and producing models of design knowledge and activity. Using these hypotheses and models, computer programs (i.e., design systems) are built that can aid designers, and carry out some portion of the design process. Thus hypotheses about design are tested and refined using design systems.

In AI in Design research all theories about design reasoning or design knowledge should be tested by building software. Consequently, the level of detail of such a theory must be such that it is actually implementable. It cannot be vague and only executable by knowledgeable humans. In addition, the performance of the resulting software can be tested and evaluated in order to provide feedback about the quality of the underlying ideas.

If the AI in Design field is to make progress, we need to fully understand what has been achieved. And to do this we need to be able to compare and contrast different research efforts. Design systems need to be characterized in order to be compared and contrasted. Fortunately, the lack of vagueness mentioned above allows us to characterize AI in Design research, and design systems in particular. As a beneficial side-effect, this is particularly useful for teaching about design.

The goals of this paper are:

• to introduce a small, representative sample from the literature in AI in Design;
• to present a perspective of design based on that literature;
• to provide a set of questions that can be used for pedagogical purposes to analyse and categorize computational research into design.

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