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Evolutionary learning of novel grammars for design improvement

Published online by Cambridge University Press:  27 February 2009

John S. Gero ,
Sushil J. Louis  and
Sourav Kundu
John S. Gero
Affiliation:
Key Centre of Design Computing, Department of Architectural and Design Science, University of Sydney, Sydney NSW 2006, Australia
Sushil J. Louis
Affiliation:
Key Centre of Design Computing, Department of Architectural and Design Science, University of Sydney, Sydney NSW 2006, Australia
Sourav Kundu
Affiliation:
Key Centre of Design Computing, Department of Architectural and Design Science, University of Sydney, Sydney NSW 2006, Australia
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Abstract

This paper focuses on that form of learning that relates to exploration, rather than generalization. It uses the notion of exploration as the modification of state spaces within which search and decision making occur. It demonstrates that the genetic algorithm formalism provides a computational construct to carry out this learning. The process is exemplified using a shape grammar for a beam section. A new shape grammar is learned that produces a new state space for the problem. This new state space has improved characteristics.

Keywords

EvolutionGenetic AlgorithmsLearningShape Grammars
Type
Articles
Information
AI EDAM , Volume 8 , Issue 2 , Spring 1994 , pp. 83 - 94
Copyright
Copyright © Cambridge University Press 1994

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References

REFERENCES

Arciszewski, T., Mustafa, Z., & Ziarko, W. (1987). A methodology of design knowledge acquisition for use in learning expert systems. Man-Machine Studies 27, 23–32.CrossRefGoogle Scholar
Cabonell, J.G. (1990). Introduction: Paradigms for machine learning. In Machine Learning Paradigms and Methods (Carbonell, J., Ed.), pp. 1–10. MIT/Elsevier, Cambridge, MA.Google Scholar
Gero, J.S. (1987). Prototypes: A new schema for knowledge based design. Working Paper, Architectural Computing Unit, Department of Architectural Science, University of Sydney, Sydney.Google Scholar
Gero, J.S. (1990). Design Prototypes: A knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Gero, J.S. (1992). Creativity, emergence and evolution in design. In Preprints: Second International Round-Table Conference on Computational Models of Creative Design (Gero, J.S., and Sudweeks, F., Eds.), pp. 1–28. Department of Architectural and Design Science, University of Sydney.Google Scholar
Gero, J.S., & Kumar, B. (1993). Expanding design spaces through new design variables. Design Studies 14(2), 210–221.CrossRefGoogle Scholar
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA.Google Scholar
Gunaratnam, D.S., & Gero, J.S. (1993). Neural network learning in structural engineering applications. In Computing in Civil and Building Engineering, (Cohen, L.F., Ed.), Vol. 2, pp. 1448–1455. ASCE, New York.Google Scholar
Holland, J. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.Google Scholar
Louis, S.J., & Rawlins, G.J.E. (1992). Syntactic analysis of convergence in genetic algorithms. In Foundations of Genetic Algorithms 2 (Whitley, D., Ed.), pp. 141–152. Morgan Kaufmann, San Mateo, CA.Google Scholar
Mackenzie, C.A. (1989). Inferring relational design grammars. Environ, and Planning B: Planning and Design 16, 252–287.CrossRefGoogle Scholar
Mackenzie, C.A. (1991). Function and Structure Relationships and Transformations in Design Processes. Ph.D. Thesis, Department of Architectural and Design Science, University of Sydney, Sydney.Google Scholar
Mackenzie, C.A., & Gero, J.S. (1987). Learning design rules from decisions and performances. Artif. Intelligence Eng. 2(1), 2–10.CrossRefGoogle Scholar
Maher, M.L., & Li, H. (1992). Automatically learning preliminary design knowledge from design examples. Microcomputers Civil Eng. 7, 73–80.CrossRefGoogle Scholar
Maher, M.L., & Li, H. (1993). Adapting conceptual clustering for preliminary structural design. In Computing in Civil and Building Engineering, (Cohen, L.F., Ed.), Vol. 2, pp. 1432–1439. ASCE, New York.Google Scholar
McLaughlin, S., & Gero, J.S. (1987). Learning from characterised designs. In Artificial Intelligence in Engineering: Tools and Techniques (Sriram, D. and Adey, R., Eds.), pp. 347–359. CM Publications, Southampton.Google Scholar
Mitchell, T.M., Carbonell, J.G., & Michalski, R.S. (Eds.). (1986). Machine Learning: A Guide to Current Research. Kluwer, Boston.CrossRefGoogle Scholar
Quinlan, J.R. (1979). Discovering rules by induction from a large collection of examples. In Expert Systems in the Micro-Electronic Age (Michie, D., Ed.), pp. 168–201. Edinburgh University Press, Edinburgh.Google Scholar
Quinlan, J.R. (1986). Induction of decision trees. Machine Learn. 1 81–106.CrossRefGoogle Scholar
Radford, A.D., & Gero, J.S. (1988). Design by Optimization in Architecture, Building, and Construction. Van Nostrand Reinhold, New York.Google Scholar
Rao, R., & Lu, S. (1993). A knowledge-based equation discovery system for engineering domains. IEEE Expert 8(4), 37–42.Google Scholar
Reich, Y. (1991). Design knowledge acquisition: task analysis and a partial implementation. Knowledge Acquisition 3, 237–254.CrossRefGoogle Scholar
Stiny, G., & Gips, J. (1978). Algorithmic Aesthetics: Computer Models for Criticism and Design in the Arts. University of California Press, Berkeley.Google Scholar