Learning Concepts and Language for a Baby Designer

  • Madan Mohan Dabbeeru
  • Amitabha Mukerjee


We introduce the “baby designer enterprise” with the objective of learning grounded symbols and rules based on experience, in order to construct the knowledge underlying design systems. In this approach, conceptual categories emerge as abstractions on patterns arising from functional constraints. Eventually, through interaction with language users, these concepts get names, and become true symbols. We demonstrate this approach for symbols related to insertion tasks and tightness of fit. We show how a functional distinction - whether the fit is tight or loose - can be learned in terms of the diameters of the peg and the hole. Further, we observe that the same category distinction can be profiled differently - e.g. as a state (clearance), or as a process (the act of insertion). By having subjects describe their experience in unconstrained speech, and associating words with the known categories for tight and loose, the frequencies of words associated with these can be discriminated. The resulting linguistic labels learned show that for the state profile, the words “tight” and “loose” emerge, and for the action, we get “tight” and “easy”. Once an initial grounded symbol is available, it is argued that knowledge-based systems based on such symbols can be sanctioned by its semantics, as well as its syntax, leading to more flexible usage.


Design Space Learn Concept Image Schema Design Knowledge Linguistic Label 
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. 1.
    Ahmed, S., Wallace, K.M., Blessing, L.T.: Understanding the differences between how novice and experienced designers approach design tasks. Research in Engineering Design 14(1), 1–11 (2003)Google Scholar
  2. 2.
    Barsalou, L.: Perceptual symbol systems. Behavioral and Brain Sciences 22, 577–660 (1999)Google Scholar
  3. 3.
    Bishop, C.: Pattern recognition and machine learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  4. 4.
    Bloom, P.: How children learn the meanings of words. MIT Press, Cambridge (2000)Google Scholar
  5. 5.
    Bohm, M.R., Stone, R.B., Szykman, S.: Enhancing virtual product representations for advanced design repository systems. Journal of Computing and Information Science in Engineering 5(4), 360–372 (2005)CrossRefGoogle Scholar
  6. 6.
    Campbell, M.I., Cagan, J., Kotovsky, K.: Agent-based synthesis of electro-mechanical design configurations. Journal of Mechanical Design 122(1), 61–69 (2000)CrossRefGoogle Scholar
  7. 7.
    Chakrabarti, A., Sarkar, P., Leelavathamma, B., Nataraju, B.S.A.: Functional representation for aiding biomimetic and artificial inspiration of new ideas. AIEDAM 19(2), 113–132 (2005)CrossRefGoogle Scholar
  8. 8.
    Deb, K., Srinivasan, A.: Innovization: innovative design principles through optimization. Tech. Rep. Kangal, 2005007. IIT Kanpur (2007)Google Scholar
  9. 9.
    Ericsson, K.: Expertise. MIT Encyclopedia of Cognitive Science (1999)Google Scholar
  10. 10.
    Evans, V., Green, M.: Cognitive Linguistics: An Introduction. Edinburgh University Press (2006)Google Scholar
  11. 11.
    Gero, J.S., Fujii, H.A.: Computational framework for concept formation for a situated design agent. Knowledge-Based Systems 13(6), 361–368 (2000)CrossRefGoogle Scholar
  12. 12.
    Gorti, S.R., Sriram, R.D.: From symbol to form: a framework for conceptual design. Computer-Aided Design 28(11), 853–870 (1996)CrossRefGoogle Scholar
  13. 13.
    Gross, M.D.: Design as Exploring Constraints. PhD thesis, Department of Architecture. MIT, Cambridge (1986)Google Scholar
  14. 14.
    Guha, P., Mukerjee, A.: Language Label Learning for Visual Concepts. Discovered from Video Sequences. Springer, Heidelberg (2008)Google Scholar
  15. 15.
    Jurafsky, D., Martin, J., Kehler, A.: Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. MIT Press, Cambridge (2000)Google Scholar
  16. 16.
    Keffy. Wiktionary: Frequency lists for TV and movie scripts (2006), (accessed February 10, 2010)
  17. 17.
    Kurtoglu, T., Campbell, M., Gonzales, J., Bryant, C., Stone, R.: Capturing empirically derived design knowledge for creating conceptual design configurations. In: Proceedings of the ASME Design Engineering Technical Conferences And Computers In Engineering Conference. DETC2005-84405, Long Beach, CA (2005)Google Scholar
  18. 18.
    Langacker, R.: An introduction to cognitive grammar. Cognitive science 10(1), 1–40Google Scholar
  19. 19.
    Langacker, R.: Cognitive Grammar: A Basic Introduction. Oxford University Press, USA (2008)Google Scholar
  20. 20.
    Lawson, B.: Schemata, gambits and precedent: some factors in design expertise. Design Studies: Expertise in Design 25(5), 443–457 (2004)CrossRefGoogle Scholar
  21. 21.
    Martinetz, T.M., Berkshire, S.G., Schulten, K.J.: Neural gas network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4, 558–569 (1993)CrossRefGoogle Scholar
  22. 22.
    Moss, J., Cagan, J., Kotovsky, K.: Learning from design experience in an agent-based design system. Research in Engineering Design 15(2), 77–92 (2004)Google Scholar
  23. 23.
    Mukerjee, A., Dabbeeru, M.M.: The birth of symbols in design. In: Proceedings of DETC 2009, ASME Design Engineering Technical Conferences (2009)Google Scholar
  24. 24.
    Nanda, J., Thevenot, H., Simpson, T., Stone, R., Bohm, M., Shooter, S.: Product family design knowledge representation, aggregation, reuse, and analysis. AIEDAM 21(02), 173–192 (2007)CrossRefGoogle Scholar
  25. 25.
    Park, S., Gero, J.: Qualitative representation and reasoning about shapes. In: Visual and Spatial Reasoning in Design, Sydney, Australia, vol. 99, pp. 55–68 (1999)Google Scholar
  26. 26.
    Sarkar, S., Dong, A., Gero, J.S.: Design optimization problem reformulation using singular value decomposition. Journal of Mechanical Design 131(8), 081006–1–10 (2009)Google Scholar
  27. 27.
    Satish, G., Mukerjee, A.: Acquiring linguistic argument structure from multimodal input using attentive focus. In: 7th IEEE International Conference on Development and Learning, ICDL 2008, pp. 43–48 (2008)Google Scholar
  28. 28.
    Schoen, D.A.: Designing: Rules, types and words. Design studies 9(3), 181–190 (1988)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Steels, L.: Evolving grounded communication for robots. Trends in Cognitive Science 7(7), 308–312 (2003)CrossRefGoogle Scholar
  30. 30.
    Yaner, P., Goel, A.: Analogical recognition of shape and structure in design drawings. AIEDAM 22(2), 117–128 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Netherlands 2011

Authors and Affiliations

  • Madan Mohan Dabbeeru
    • 1
  • Amitabha Mukerjee
    • 1
  1. 1.Indian Institute of Technology KanpurIndia

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