An Intelligent Assistant for Conceptual Design

Informed Search Using a Mapping of Abstract Qualities to Physical Form
  • Kimberle Koile
Conference paper


In early stages of design, the language used is often very abstract. In architectural design, for example, architects and their clients use experiential terms such as “private” or “open” to describe spaces. The Architect’s Collaborator (TAC) is a prototype design assistant that supports iterative design refinement using abstract, experiential terms. TAC explores the space of possible designs in search of solutions satisfying specified abstract goals by employing a strategy we call dependency-directed redesign: It evaluates a design with respect to a set of goals, uses an explanation of the evaluation to guide proposal and refinement of design repair suggestions, then carries out the repair suggestions to create new designs.


Physical Form Visual Openness Experiential Quality Front Door Dine Room 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Alexander, C, Ishikawa, S, Silverstein, M, Jacobsen, M, Fiksdahl-King, I and Angel, S: 1977, A Pattern Language, Oxford University Press, New York.Google Scholar
  2. Cui, Z and Randell, D: 1992, Qualitative simulation based on a logical formalism of space and time, AAAI `92, pp. 679–684.Google Scholar
  3. Flemming, U and Mandavi, A: 1993, Simultaneous form generation and performance evaluation: A ‘two-way’ inference approach, in U Flemming and S Van Wyk (eds), CAAD Futures ’93, North-Holland, 161–174.Google Scholar
  4. Giretti, A and Spalazzi, L: 1997, ASA: A conceptual design-support system, Engineering Applications of Artiificial Intelligence 10(1): 99–111.CrossRefGoogle Scholar
  5. Goel, AK: 1991, A model-based approach to case adaptation, Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society, Lawrence Erlbaum, pp. 143–148.Google Scholar
  6. Gross, MD: 1996, The electronic cocktail napkin--a computational environment for working with design diagrams, Design Studies 17: 53–69.CrossRefGoogle Scholar
  7. Hertzberger, H: 1993, Lessons for Students in Architecture, Uitgeverij Publishers, Rotterdam.Google Scholar
  8. Hillier, B and Hanson, J: 1984, The Social Logic of Space, Cambridge University Press.CrossRefGoogle Scholar
  9. Hildebrand, G: 1991, The Wright Space: Pattern and Meaning in Frank Lloyd Wright’s Houses, University of Washington Press, Seattle.Google Scholar
  10. Hua, K, Faltings, B and Smith, I: 1996, CADRE: Case-based geometric design, Artificial Intelligence in Engineering 10: 171–183.CrossRefGoogle Scholar
  11. Kincaid, DS: 1997, An Arithmetical Model of Spatial Definition, Master of Architecture Thesis, Dept. of Department of Architecture, Massachusetts Institute of Technology.Google Scholar
  12. Koile, K: 1997, Design conversations with your computer: evaluating experiential qualities of physical form, in R Junge (ed), CAAD Futures ’97, Kluwer, pp. 203–218.CrossRefGoogle Scholar
  13. Koile, K: 2001, The Architect’s Collaborator: Toward Intelligent Tools for Conceptual Design, PhD Thesis, Dept. of EECS, MIT.Google Scholar
  14. Mandavi, A and Suter, G: 1997, On implementing a computational facade design support tool, Environment and Planning B 24: 493–508.CrossRefGoogle Scholar
  15. Mandavi, A and Suter, G: 1998, On the implications of design process views for the development of computational design support tools, Automation and Construction 7(2–3): 189–204.CrossRefGoogle Scholar
  16. Oxman, R: 1996, Design by re-representation: A model of visual reasoning in design, Design Studies 18(4): 329–347.CrossRefGoogle Scholar
  17. Prabhakar, S and Goel, AK: 1998, Functional modeling for enabling adaptive design of devices for new environments, Artificial Intelligence in Engineering 12: 417–444.CrossRefGoogle Scholar
  18. Saderdoti, E: 1974, Planning in a hierarchy of abstraction spaces, Artificial Intelligence 5(2): 115–135.CrossRefGoogle Scholar
  19. Simmons, RG: 1992, The roles of associational and causal reasoning in problem solving, Artiifiicial Intelligence 53(2–3): 159–208.MathSciNetzbMATHCrossRefGoogle Scholar
  20. Simoff, SJ and Maher, ML: 1998, Designing with the activity/space ontology, in JS Gero and F Sudweeks (eds), Artiifiicial Intelligence in Design ’98, Kluwer, 23–43.CrossRefGoogle Scholar
  21. Smith, I, Stalker, R and Lottaz, C: 1996, Creating design objects from cases for interactive spatial composition, in JS Gero (ed), Artificial Intelligence in Design ’96, Kluwer, 97–116.CrossRefGoogle Scholar
  22. Stallman, R and Sussman, G: 1977, Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis, Artiifiicial Intelligence 9: 135–196.zbMATHCrossRefGoogle Scholar
  23. Sussman, GJ: 1975, A Computer Model of Skill Acquisition, American Elsevier, New York.Google Scholar
  24. Voss, A and Oxman, R: 1996, A study of case adaptation systems, in JS Gero (ed), Artificial Intelligence in Design ’96, Kluwer, 173–189.CrossRefGoogle Scholar
  25. Wright, FL: 1954, The Natural House, Horizon Press, New York.Google Scholar
  26. Zeisel, J and Welch, P: 1981, Housing Designed for Families: A Summary of Research, Joint Center for Urban Studies of MIT and Harvard University, Cambridge, MA.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2004

Authors and Affiliations

  • Kimberle Koile
    • 1
  1. 1.Massachusetts Institute of TechnologyUSA

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