Abstract
The engineering task of configuration design, the combination of pre-defined domain entities into a system that meets some specified requirements, is ill-defined: there is no computationally expressible algorithm available for consistently producing adequate designs.
This suggests that Artificial Intelligence (AI) techniques must be applied to produce an automated design tool. However, past attempts at construction have relied on hard computing techniques, usually in the form of ‘hard-wiring’ design rules into a knowledge base, with the obvious necessity for all these rules to be available in an explicit form. This is rarely the case outside simple domains.
This design ‘knowledge’, then, is the crucial factor in such systems, and the difficulties involved in its acquisition and expression are persistent obstacles to their construction. In this paper, we show how a soft computing approach (artificial neural networks) can be applied to the problem of capturing and expressing some of the more nebulous elements of this knowledge, which is then incorporated within a conventional hard computing framework to provide a useful design tool. The use of neural networks would seem to be particularly agreeable in a design context, as they display some emergent properties associated with aspects of design creativity.
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© 1998 Springer-Verlag London
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Potter, S., Chawdhry, P.K., Culley, S.J. (1998). Soft vs. Hard Computational Issues in Configuration Design. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds) Soft Computing in Engineering Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-0427-8_34
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DOI: https://doi.org/10.1007/978-1-4471-0427-8_34
Publisher Name: Springer, London
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