A concept for a constraint-based representation of functional and geometric design knowledge
SMA '91 Proceedings of the first ACM symposium on Solid modeling foundations and CAD/CAM applications
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Robot Motion Planning
A qualitative discretization for two-body contacts
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Discovering implicit constraints in design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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Traditionally it has been assumed "Design Specifications" are given a priori, and constitute the primary constraints on the process of search that is design. This view is under challenge in emergentist accounts of design, where it is seen that for well-understood functional needs, experienced designers are able to come up with good designs very quickly. It is hypothesized that this is possible because search is minimized using novel functional constraints that emerge from experience. These emergent aspects are difficult to model in a computational framework, and this work is a preliminary attempt in this direction. "Well-understood functions" are assumed to be quantifiable in terms of some performance metrics, which permits us to identify regions of high functional validity as emergent constraint regions in the design space. In addition, designers often change the design space itself, and negotiate the initial specs in many ways. We show that small changes in the design space may result in large changes in this mapping, which is why such emergent knowledge is limited to a specific embodiment. By introducing such measures into future solid modeling systems, it may reduce the human designer's search to the more ill-posed aspects of the problem.