1 Copyright © 2000 by ASME Proceedings of DETC’00 ASME 2000 Design Engineering Technical Conferences and Computers and Information in Engineering Conference Baltimore, Maryland, September 10-13, 2000 DETC2000/CIE-14617 SIGNPOSTING: AN AI APPROACH TO SUPPORTING HUMAN DECISION MAKING IN DESIGN Martin Stacey Department of Computer and Information Sciences De Montfort University, Milton Keynes, UK P John Clarkson Engineering Design Centre Engineering Department Cambridge University, Cambridge, UK Claudia Eckert Engineering Design Centre Engineering Department Cambridge University, Cambridge, UK ABSTRACT Artificial intelligence provides powerful techniques for formalising the art of engineering problem solving: for modelling products, describing task structures, and representing problem solving expertise as inference knowledge and control knowledge. Signposting systems extend the scope of these methods beyond automatic design by using them to provide both information and guidance for decision- making by human designers. This paper outlines the application of AI methods according to cognitive engineering considerations, to the development of knowledge management tools for engineering design. These tools go beyond conventional knowledge management and decision support approaches by supplying both inference knowledge and strategic problem solving knowledge to the user, as well as information about the state of the design. By focusing on tasks and on the dependencies between design parameters, signposting systems support contingent and flexible organisation of activities. Such tools can support product modelling, design process planning and capturing expert design knowledge, in a form that can be used directly to guide the organisation of design activities and the performance of individual tasks. A key element of this approach is the incremental acquisition of product models, task structures and problem solving knowledge by defining variant cases. 1 INTRODUCTION One way to view symbolic artificial intelligence is that it is the attempt to understand the art of problem solving in rigorous computational terms. This view of the AI project, focusing on knowledge and mechanism, is broader than the behaviour-centred view of AI as the construction of intelligent systems. It offers a different perspective on how to apply the concepts and methods of AI to problems too subtle and open-ended for independent AI reasoning systems, such as conceptual design in engineering, which we are applying in the development of signposting systems. The demands and pressures on many engineering designers are rapidly increasing, as they have to obey more and more regulations, consider a wider variety of factors and best practice procedures (see for instance Huang, 1996), and strive ever harder to reduce lead times and avoid mistakes that cause costly revisions. But many engineering companies possess a great deal of experience and expertise; the challenge is to retain this expertise despite staff turnover, develop it, and deploy it where and when it is needed. But how can effective corporate knowledge management in design be achieved? While expert designers have a wide knowledge of facts and examples, their essential expertise lies in skills for analysing and solving particular kinds of problems. While obtaining information is a major drain on engineers’ time, possessing the necessary range of expertise to integrate this information considering all the important issues can be a much less tractable problem. Knowledge management and decision support systems can provide far more assistance to designers by supporting problem analysis and design synthesis as well as fact gathering. AI has been applied to corporate management of problem solving expertise: one important commercial purpose for expert systems is to make explicit and encode the problem-solving skills of human experts for reuse by non-experts. This can be motivated by the desire to