Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-06-04T23:02:37.158Z Has data issue: false hasContentIssue false
  • English
  • Français

Sharing the learning activity using intelligent CAD

Published online by Cambridge University Press:  27 February 2009

Sandra M. Duffy  and
Alex H.B. Duffy
Sandra M. Duffy
Affiliation:
CAD Centre, University of Strathclyde, Glasgow, Scotland, U.K.
Alex H.B. Duffy
Affiliation:
CAD Centre, University of Strathclyde, Glasgow, Scotland, U.K.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In this paper the need for Intelligent Computer Aided Design (Int.CAD) to jointly support design and learning assistance is introduced. The paper focuses on presenting and exploring the possibility of realizing “learning” assistance in Int.CAD by introducing a new concept called Shared Learning. Shared Learning is proposed to empower CAD tools with more useful learning capabilities than that currently available and thereby provide a stronger interaction of learning between a designer and a computer. “Controlled” computational learning is proposed as a means whereby the Shared Learning concept can be realized. The viability of this new concept is explored by using a system called PERSPECT. PERSPECT is a preliminary numerical design tool aimed at supporting the effective utilization of numerical experiential knowledge in design. After a detailed discussion of PERSPECT's numerical design support, the paper presents the results of an evaluation that focuses on PERSPECT's implementation of “controlled” computational learning and ability to support a designer's need to learn. The paper then discusses PERSPECT's potential as a tool for supporting the Shared Learning concept by explaining how a designer and PERSPECT can jointly learn. There is still much work to be done before the full potential of Shared Learning can be realized. However, the authors do believe that the concept of Shared Learning may hold the key to truly empowering learning in Int.CAD.

Keywords

Design AssistanceLearning AssistanceShared LearningControlled Computational Learning
Type
Articles
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 83 - 100
Copyright
Copyright © Cambridge University Press 1996

References

REFERENCES

Arbab, F. (1989). Design object modelling. In Intelligent CAD I (Yoshikawa, H. and Gossard, D., Eds.), pp. 3–12. North Holland.Google Scholar
Becker, R.A., Chambers, J.M., & Wilks, A.R. (1988). The New S Language: A programming environment for data analysis and graphics. Wadsworth and Brooks/Cole Advanced Books and Software, Pacific Grove, CA.Google Scholar
Bhatta, S.R., & Goel, A.K. (1994). Discovery of physical principles from design experiences. AIEDAM 8(2), 113–123.CrossRefGoogle Scholar
Bisson, G. (1992). Conceptual clustering in a first order logic representation. ECAI-92 (Neumann, B., Ed.), John Wiley and Sons, Chichester, England, U.K.Google Scholar
CAL'94 (1994). Proc. Comput. Aided Learn. Eng. (Eames, l.W. and Johnson, A.R., Eds.). University of Sheffield, Sheffield, U.K.Google Scholar
Coyne, R.D., & Newton, S. (1989). A tutorial on neural networks and expert systems for design. In Expert Systems in Engineering, Architecture and Construction (Gero, J.S. and Sudweeks, F., Eds.), pp. 321–337. University of Sydney, Sydney, Australia.Google Scholar
de Siervo, F., & de Leva, F. (1976). Modern trends in selecting and designing Francis turbines. Water Power and Dam Construction 28(8), 28–35.Google Scholar
de Siervo, F., & de Leva, F. (1977). Modern trends in selecting and designing Kaplan turbines. Water Power and Dam Construction 29(12), 51–56.Google Scholar
Duffy, A.H.B. (1986). Computer Modelling of Early Stage Numerical Ship Design Knowledge and Expertise. Ph.D. thesis, Department of Ship and Marine Technology, University of Strathclyde, Glasgow, U.K.Google Scholar
Duffy, A.H.B., Ed. (1993). Special Issue on Machine Learning in Design. Artif. Intell. Eng. 8(3).Google Scholar
Duffy, S.M. (1994a). Design reuse process model. CAD Centre, University of Strathclyde, Glasgow, Scotland, U.K. CADC/R/94–08, CADC/REUSE/94–01.Google Scholar
Duffy, S.M. (1994b). Supporting domain exploration. CAD Centre, University of Strathclyde, Glasgow, Scotland, U.K. CADC/R/94–09, CADC/REUSE/94–02.Google Scholar
Duffy, A.H.B., Brown, D.C., & Maher, M.L., Eds. (1994). Machine learning in design workshop. Third Int. Conf. Artif. Intell. Design (A1D'94).Google Scholar
Duffy, A.H.B.,& Kerr, S.M. (1993). Customised perspectives of past designs from automated group rationalization. AIENG 8(3).Google Scholar
Duffy, A.H.B., & MacCallum, K.J. (1989). Computer representation of numerical expertise for preliminary design. Marine Technology 26(4), 289–302.Google Scholar
Duffy, S.M., Duffy, A.H.B., & MacCallum, K.J. (1995). A design reuse model. Proc. Tenth Int. Conf. Eng. Design, 490–495.Google Scholar
Europe, S.. (1991). S-plus: User's manual, Vol. 1 and Vol. 2. Statistical Sciences UK Ltd., Oxford, U.K.Google Scholar
Fruchter, R., & Gluck, J. (1989). Adaptive multilevel connections in a training learning system for structural analysis. In Artificial Intelligence in Design (Gero, J.S., Ed.), pp. 209–227. Springer Verlag, NY.Google Scholar
Galle, P., & Kovacs, L.B. (1992). Introspective observations of sketch design. Design Studies 13(3), 229–272.CrossRefGoogle Scholar
Galle, P., Hansen, J.P., Jansen, S.E., Rubow, M., & Wright, E.C. (1975). Housing patterns: Pattern collection II. Pattern Theory 5, Royal Danish Academy of Fine Arts, School of Architecture, Copenhagen, Denmark.Google Scholar
Gero, J.S., Ed. (1991). Artificial intelligence in design (AID'91). Proc. First Int. Conf. Artif. Intell. Design (AID'91). Butterworth-Heinemann, Oxford, U.K.Google Scholar
Gero, J.S., Ed. (1992). Artificial intelligence in design (AID'92). Proc. Second Int. Conf. Artif. Intell. Design (AID'92). Kluwer Academic Publishers, Dordrecht, The Netherlands.Google Scholar
Gilfillan, A.W. (1969). The economic design of bulk cargo carriers. In Transactions of the Royal Institution of Naval Architects, 3, 113–140.Google Scholar
Guan, X., MacCallum, K.J., Duffy, A.H.B., & Stevenson, D. (1995). Supporting abstractions in a computational environment for geometric configuration design. ICED'95. Proc. Tenth Int. Conf. Eng. Design, Prague, 22–24 August 1995, Vol. 4, 1557–1558.Google Scholar
Guena, F., & Zreik, K. (1989). An architect assisted architectural design system. Proc. Fourth Int. Conf. Applications of Artif. Intell. Eng., 159–179.Google Scholar
Kamal, S.Z., Mistree, F., Sorab, J., & van Arsdale, W.E. (1989). The development of an inductive learning system for design using experimental information. In Artificial Intelligence in Design (Gero, J.S., Ed.), pp. 521–538. Springer-Verlag, Berlin.Google Scholar
Kerr, S.M. (1993). Customized viewpoint support for the utilization of experiential knowledge in design. Ph.D. thesis, CAD Centre, Department of Design, Manufacture and Engineering Management, University of Strathclyde, Glasgow, U.K.Google Scholar
Kerr, S.M., & Duffy, A.H.B. (1992). Dynamic memory by automating rationalization of past designs. In Machine Learning in Design Workshop '92 (Maher, M.L., Brown, D.C., and Duffy, A.H.B., Eds.), Carnegie Mellon University, Pittsburgh, PA.Google Scholar
Kocabas, S. (1991). A review of learning. The Knowledge Engineering Rev. 6(3), 195–222.CrossRefGoogle Scholar
Lu, S.C.-Y., & Chen, K. (1987). A machine learning approach to the automatic synthesis of mechanistic knowledge for engineering decision-making. AIEDAM 1(2), 109–118.CrossRefGoogle Scholar
Lugaresi, A., & Massa, A. (1987). Designing Francis turbines: Trends in the last decade. Water Power and Dam Construction 39(11), 23–28.Google Scholar
Lugaresi, A., & Massa, A. (1988). Designing Kaplan turbines: Trends in the last decade. Water Power and Dam Construction 40(5), 12–17.Google Scholar
MacCallum, K.J., Duffy, A., & Green, S. (1987). An intelligent concept design assistant. In Design Theory for CAD (Yoshikawa, H. and Warman, E.A., Eds.), pp. 301–317. Elsevier Science.Google Scholar
MacCallum, K.J., & Duffy, A. (1990). Representing and using numerical empiricism in design. Proc. Int. Conf. Artif. Intell. Eng., 115–136.Google Scholar
Mackenzie, C.A., & Gero, J.S. (1987). Learning design rules from decisions and performances. Artif. Intell. Eng. 2(1), 2–10.CrossRefGoogle Scholar
Maher, M.L., Brown, D.C., & Duffy, A.H.B., Eds. (1994). AIEDAM 8(2).Google Scholar
Maher, M.L., & Li, H. (1994). Learning design concepts using machine learning techniques. AIEDAM 8(2), 95–111.CrossRefGoogle Scholar
Maher, M.L., Brown, D.C., & Duffy, A.H.B., Eds. (1992). Machine learning in design workshop. Second Int. Conf. Artif. Intell. Design (AID'92).Google Scholar
McBride, P. (1991). The Lispview grapher. Technical Report M/S MTV 12–40, SunPro, A Sun Microsystems, Inc., Mountain View, CA.Google Scholar
McLaughlin, S., & Gero, J.S. (1987). Acquiring expert knowledge from characterised designs. AIEDAM 1(2), 73–87.CrossRefGoogle Scholar
Mitchell, T.M., Mahadevan, S., & Steinberg, L.I. (1985). LEAP: A learning apprentice for VLSI design. Proc. Ninth Int. Joint Conf. on Artif. Intell., Vol. 1, 573–580.Google Scholar
Mostow, J., & Bhatnagar, N. (1987). FAILSAFE: A floor planner that uses explanation based learning to learn from its failures. Proc. Int. Joint Conf. Artif. Intell. (IJCAI87), 249–255.Google Scholar
Mostow, J., & Roy, S. (1987). Machine learning for knowledge acquisition in design systems. Extended abstract presented at Proc. IFIP WG5.2 on Intell. CAD.Google Scholar
Motard, R.L. (1974). Design: Chemical engineering. In Basic Questions of Design Theory, (Spillers, W.R., Ed.), pp. 143–146. North-Holland, Amsterdam.Google Scholar
Oxman, R. (1990). Prior knowledge in design: A dynamic knowledge-based model of design and creativity. Design Studies 11(1), 17–28.CrossRefGoogle Scholar
Persidis, A. (1989). Modelling of abstraction for computer aided design. Ph.D. thesis, CAD Centre, University of Strathclyde, Glasgow, U.K.Google Scholar
Persidis, A., & Duffy, A. (1991). Learning in engineering design. In Intelligent CAD III (Yoshikawa, H., Arbab, F. and Tomiyama, T., Eds.), pp. 251–272. Elsevier Science Publishers B.V. (North-Holland), Amsterdam.Google Scholar
Radford, A., Hung, P., & Gero, J.S. (1984). New rules of thumb from computer-aided structural design: Acquiring knowledge for expert systems. In CAD'84 (Wexler, J., Ed.), pp. 558–566. Butterworths, Guildford.CrossRefGoogle Scholar
Rao, R.B., & Lu, S.C.-Y. (1993). A knowledge-based equation discovery system for engineering domains. IEEE Expert 8(4), 37–42.Google Scholar
Reich, Y. (1991). Building and Improving Design Systems: A machine learning approach. Ph.D. thesis, Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.Google Scholar
Schank, R. (1982). Dynamic Memory: A theory of remindings and learning in computers and people. Cambridge University Press, Cambridge, U.K.Google Scholar
Scott, J.R. (1967). Some aspects of multiple regression analysis. Vickers Ltd., St. Albans Test Tank, St. Albans, U.K.Google Scholar
Steinberg, E.R. (1991). Computer assisted instruction: A synthesis of theory, practice and instruction. Lawrence Erlbaum Associates, Hillsdale, NJ.Google Scholar
Warman, E.A. (1978). Computer aided design: Intersection of ideas. In Artificial Intelligence and Pattern Recognition in Computer Aided Design (Latombe, J.C., Ed.), pp. 1–18, North-Holland, Amsterdam.Google Scholar
Watson, D.G.M. (1962). Estimating preliminary dimensions in ship design. Transactions of the Institution of Engineers and Shipbuilders in Scotland (IESS) 105, 110–161.Google Scholar
Watson, D.G.M., & Gilfillan, A.W. (1977). Some ship design methods. Naval Architect 279–324.Google Scholar
Willem, R.A. (1990). Design and science. Design Studies 11(1), 43–47.CrossRefGoogle Scholar
Yoshikawa, H., Arbab, F., & Tomiyama, T., Eds. (1991). Intelligent CAD III. Elsevier Science Publishers B.V. (North-Holland), Amsterdam, The Netherlands.Google Scholar