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Discovering implicit constraints in design

Published online by Cambridge University Press:  07 February 2011

Madan Mohan Dabbeeru  and
Amitabha Mukerjee
Madan Mohan Dabbeeru
Affiliation:
Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Amitabha Mukerjee
Affiliation:
Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
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Abstract

Designers who are experts in a given design domain are well known to be able to Immediately focus on “good designs,” suggesting that they may have learned additional constraints while exploring the design space based on some functional aspects. These constraints, which are often implicit, result in a redefinition of the design space, and may be crucial for discovering chunks or interrelations among the design variables. Here we propose a machine-learning approach for discovering such constraints in supervised design tasks. We develop models for specifying design function in situations where the design has a given structure or embodiment, in terms of a set of performance metrics that evaluate a given design. The functionally feasible regions, which are those parts of the design space that demonstrate high levels of performance, can now be learned using any general purpose function approximator. We demonstrate this process using examples from the design of simple locking mechanisms, and as in human experience, we show that the quality of the constraints learned improves with greater exposure in the design space. Next, we consider changing the embodiment and suggest that similar embodiments may have similar abstractions. To explore convergence, we also investigate the variability in time and error rates where the experiential patterns are significantly different. In the process, we also consider the situation where certain functionally feasible regions may encode lower dimensional manifolds and how this may relate to cognitive chunking.

Keywords

ChunkingDesign ChangeDesign Space ShrinkingFunctional EmergenceImplicit Constraints
Type
Articles
Information
AI EDAM , Volume 25 , Issue 1 , February 2011 , pp. 57 - 75
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

Ahmed, S., Wallace, K.M., & Blessing, L.T. (2003). Understanding the differences between how novice and experienced designers approach design tasks. Research in Engineering Design 14(1), 1–11.CrossRefGoogle Scholar
Bishop, C. (2006). Pattern Recognition and Machine Learning. New York: Springer.Google Scholar
Bor, D., & Owen, A. (2007). Cognitive training: neural correlates of expert skills. Current Biology 17(3), 95–97.CrossRefGoogle ScholarPubMed
Brunetti, G., & Golob, B. (2000). A feature-based approach towards an integrated product model including conceptual design information. Computer-Aided Design 32(14), 877–887.CrossRefGoogle Scholar
Cagan, J., & Agogino, A. (1991). Dimensional Variable Expansion—a formal approach to innovative design. Research in Engineering Design 3(2), 75–85.CrossRefGoogle Scholar
Campbell, M.I., Cagan, J., & Kotovsky, K. (2003). The A-design approach to managing automated design synthesis. Research in Engineering Design 14(1), 12–24.CrossRefGoogle Scholar
Chandrasekaran, B. (2005). Representing function: relating functional representation and functional modeling research streams. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19(2), 65–74.CrossRefGoogle Scholar
Chase, W., & Simon, H. (1973). The minds eye in chess. In Visual Information Processing (Chase, W.G., Ed.), pp. 215–281. New York: Academic Press.CrossRefGoogle Scholar
Cross, N. (2004). Engineering Design Methods: Strategies for Product Design. Chichester: Wiley.Google Scholar
Dabbeeru, M.M., & Mukerjee, A. (2008). Negotiating design specifications: evolving functional constraints in mechanical assembly design. Proc. 2008 ACM Symp. Solid and Physical Modeling, SPM '08, pp. 333–338. New York: ACM.CrossRefGoogle Scholar
Dabbeeru, M.M., & Mukerjee, A. (2010). Learning concepts and language for a baby designer. Proc. 4th Int. Conf. Design Computing and Cognition. Stuttgart: Springer.Google Scholar
Ericsson, K.A., & Lehmann, A.C. (1996). Expert and exceptional performance: evidence on maximal adaptations on task constraints. Annual Review of Psychology 47, 273–305.CrossRefGoogle ScholarPubMed
Faltings, B. (1992). A symbolic approach to qualitative kinematics. Artificial Intelligence 56(2–3), 139–170.CrossRefGoogle Scholar
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11, 26–36. Accessed at http://people.arch.usyd.edu.au/john/publications/ger-prototypes/ger-aimag.htmlGoogle Scholar
Gero, J.S., & Kannengiesser, U. (2004). The situated function–behaviour–structure framework. Design Studies 4(25), 373–391.CrossRefGoogle Scholar
Gobet, F., Lane, P.C.R., Croker, S., Cheng, P.C.H., Jones, G., Oliver, I., & Pine, J.M. (2001). Chunking mechanisms in human learning. Trends in Cognitive Sciences 5(6), 236–243.CrossRefGoogle ScholarPubMed
Gobet, F., & Wood, D. (1999). Expertise, models of learning and computer-based tutoring. Computers & Education 33(2–3), 189–207.CrossRefGoogle Scholar
Goel, V. (1995). Sketches of Thought. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Gross, M., Ervin, S.M., Anderson, J.A., & Fleisher, A. (1988). Constraints: knowledge representation in design. Design Studies 9(3), 133–143.CrossRefGoogle Scholar
Gross, M.D. (1986). Design as exploring constraints. PhD Thesis. Massachusetts Institute of Technology, Department of Architecture.Google Scholar
Janssen, P.H. (2006). The role of preconceptions in design: some implications for the development of computational design tools. Proc. 2006 Design Computing and Cognition, DCC'06 (Gero, J.S., Ed.), pp. 365–383. New York: Springer–Verlag.Google Scholar
Ji, X., & Xiao, J. (2001). Planning motion compliant to complex contact states. International Journal of Robotics Research 20(6), 446–465.CrossRefGoogle Scholar
Lin, V.C., Gossard, D.C., & Light, R.A. (1981). Variational geometry in computer aided design. Computer Graphics 15(3), 171–177.CrossRefGoogle Scholar
Linsey, J., Wood, K., & Markman, A. (2008). Modality and representation in analogy. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22(2), 85–100.CrossRefGoogle Scholar
Lloyd, P., & Scott, P. (1994). Discovering the design problem. Design Studies 15, 125–140.CrossRefGoogle Scholar
Mukerjee, A., & Bhatia, P. (1995). A qualitative discretization for two-body contacts. Proc. 14th IJCAI Conf., pp. 915–921, Montreal.Google Scholar
Mukerjee, A., & Dabbeeru, M.M. (2009). The birth of symbols in design. Proc. ASME 2009 Design Engineering Technical Conf., DETC'09.Google Scholar
Oxman, R. (2002). The thinking eye: visual re-cognition in design emergence. Design Studies 23(2), 135–164.CrossRefGoogle Scholar
Poon, J., & Maher, M.L. (1996). Emergent Behaviour in Co-Evolutionary Design, pp. 703–722. Dordrecht: Kluwer Academic.Google Scholar
Schon, D.A. (1983). The Reflective Practitioner. New York: Basic Books.Google Scholar
Soufi, B., & Edmonds, E. (1996). The cognitive basis of emergence: implications for design support. Design Studies 17, 451–463.CrossRefGoogle Scholar
Spelke, E., & Hespos, S. (2002). Conceptual development in infancy: the case of containment. In Representation, Memory, and Development: Essays in Honor of Jean Mandler (Stein, N.L., Bauer, P.J., & Rabinowitz, M., Eds.), pp. 223–246. Mahwah, NJ: Erlbaum.Google Scholar
Sutherland, I.E. (1963). Sketchpad—a graphical man–machine interface. PhD Thesis. University of Cambridge.Google Scholar
Suwa, M., Gero, J., & Purcell, T. (2000). Unexpected discoveries and S-invention of design requirements: important vehicles for a design process. Design Studies 21(6), 539–567.CrossRefGoogle Scholar
Suwa, M., & Tversky, B. (1997). What do architects and students perceive in their design sketches? A protocol analysis. Design Studies 18, 385–403.CrossRefGoogle Scholar
Umeda, Y., & Tomiyama, T. (1997). Functional reasoning in design. IEEE Intelligent Systems and Their Applications 12(2), 42–48.Google Scholar
Wolter, J., & Chandrasekaran, P. (1991). A concept for a constraint-based representation of functional and geometric design knowledge. Proc. 1st ACM Symp. Solid Modeling Foundations and CAD/CAM Applications Conf., pp. 409–418.Google Scholar
Yaner, P., & Goel, A. (2008). Analogical recognition of shape and structure in design drawings. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22(2), 117–128.CrossRefGoogle Scholar