Abstract
This paper presents a methodology for identifying the relevant design elements for the synthesis of new structural designs using previous design situations and their corresponding solutions. The study is a reflection of the observation that engineers use related experience when solving new problems. The methodology is an application of transformational analogy, a form of analogical reasoning.
A prototype system STRUPLE has been designed to implement the methodology, making use of knowledge based expert systems techniques. The emphasis of STRUPLE differs from that of traditional expert systems in that the latter only use formalized o or compiled knowledge, whereas STRUPLE uses an experience data base as a knowledge supplement.
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References
Buchanan, B.G.; Shortliffe, E.H. (1984) “The MYCIN Experiments of the Stanford Heuristic Programming Project.” Addison-Wesley, MA
Davis, R.; Austin, H.; Carlbom, I.; Frawley, B.; Pruchnik, P.; Sneiderman, R.; Gilreath, A. (1981) The dipmeter advisor: Interpretation of geological signals. In: Proceedings of Seventh International Joint Conference on Artificial Intelligence
Marcus, S.; Stout, J.; McDermott, J. (1986) VT: An expert elevator designer that uses knowledge-based backtracking. Technical Report CMU-CS-86-169, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA
Kraft, A. (1984) XCON: An expert configuration system at digital equipment corporation. In: Winston, Prendergast (eds.) The AI Business: the Commercial Uses of Artificial Intelligence. The MIT Press, Cambridge, MA, pp. 41–49
Mitchell, T.; Steinberg, L.; Shulman, J. (1985) A knowledge-based approach to design. IEEE Trans. Pat. Anal. Mach. Intell. PAMI-7(5), 502–510, September
Maher, M.L. (1985) HI-RISE: A knowledge-based expert system for the preliminary structural design of high rise buildings. PhD thesis, Department of Civil Engineering, Carnegie Mellon University, Pittsburgh PA, January
Harmon P., King, D. (1985) Expert Systems: Artificial Intelligence in Business. John Wiley, New York
Carbonell, J.G. (1986) Derivational analogy: A theory of reconstructive problem solving and expertise acquisition In: Michalski, Caubonell., and Mitchell (eds.) Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann Publishers, Los Altos, CA, Vol. 1, pp. 137–162
Carbonell, J.G.: Larkin, J.H.; Reif, F. (1983) Towards a general scientific reasoning engine. Technical Report CIP No. 445, Department of Compute Science, Carnegie Mellon University, Pittsburgh, PA
Huhns, M.N.; Acosta, R.D. (1987) Argo: An analogical reasoning system for solving design problems. Technical Report MCC-AI/CAD-092-87, Microelectronics and Computer Technology Corporation, Austin, TX, April
Zhao, F. (1987) Application of AI techniques to planning structural system configurations. Master's thesis., Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, PA, March
Forgy, C.L. (1984) The OPS83 user's manual and report. Technical Report CMU-CS-84-133, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA May
Woodfill, J.; Siegal, P.; Ranstrom., J.; Meyer, M.; Epstein, R. (1985) Ingres Version 7 Reference Manual. Electronics Research Laboratory, College of Engineering, University of California at Berkeley
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Zhao, F., Maher, M.L. Using analogical reasoning to design buildings. Engineering with Computers 4, 107–119 (1988). https://doi.org/10.1007/BF01199293
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DOI: https://doi.org/10.1007/BF01199293