Signposting: an AI approach to supporting human decision making in design
Signposting: an AI approach to supporting human decision making in design
Department of Computer and Information Sciences, De Montfort University, Milton Keynes, UK.
Engineering Design Centre, Department of Engineering, University of Cambridge
Engineering Design Centre, Department of Engineering, University of Cambridge
20th Computers and Information in Engineering Conference,
Proceedings of the ASME 2000 Design Engineering Technical Conferences.
American Society of Mechanical Engineers, Baltimore, MD, 2000.
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.
KEYWORDS. Signposting, engineering design, artificial intelligence, knowledge acquisition, knowledge management, process planning, process models, design guidelines.
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Martin Stacey
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John Clarkson
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Claudia Eckert
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