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Inside the Collective Mind: Features Extraction to Support Automated Design Space Explorations

  • Lucian-Constantin UngureanuEmail author
  • Timo Hartmann
Conference paper

Abstract

The paper investigates the possibility to extract meaningful information out of natural language design conversation. This meaningful information, referred in this paper as features, represents possible design changes and solutions discussed during collaborative design sessions. Without relying on user input and without disrupting the natural course of the conversations, we envision an automatic implementation of these changes and solutions into a parametric model. The aim of such a system is to allow for an automatic design space exploration without interrupting the design sessions. In this direction, the paper employs mixed research methods which make use of quantitative and qualitative analysis. The results obtained indicate the possibility of extracting structured information to perform changes in various parametric models automatically. The paper also provides discussions around specific limitations, such as unclear precedents due to multimodality input.

Keywords

Natural language Collaborative design Design automation Design meetings 

References

  1. 1.
    Bernal, M., Haymaker, J.R., Eastman, C.: On the role of computational support for designers in action. Des. Stud. 41, 163–182 (2015)CrossRefGoogle Scholar
  2. 2.
    Burger, S., MacLaren, V., Yu, H.: The isl meeting corpus: the impact of meeting type on speech style. In: Seventh International Conference on Spoken Language Processing (2002)Google Scholar
  3. 3.
    Chen, K., Zhang, Z., Long, J., Zhang, H.: Turning from tf-idf to tf-igm for term weighting in text classification. Expert Syst. Appl. 66, 245–260 (2016)CrossRefGoogle Scholar
  4. 4.
    El-Assady, M., Gold, V., Acevedo, C., Collins, C., Keim, D.: Contovi: multi-party conversation exploration using topic-space views. In: Computer Graphics Forum, vol. 35, pp. 431–440. Wiley Online Library (2016)Google Scholar
  5. 5.
    El-Assady, M., Sevastjanova, R., Gipp, B., Keim, D., Collins, C.: Nerex: Named-entity relationship exploration in multi-party conversations. In: Computer Graphics Forum, vol. 36, pp. 213–225. Wiley Online Library (2017)Google Scholar
  6. 6.
    Fougeres, A.J., Ostrosi, E.: Intelligent agents for feature modelling in computer aided design. J. Comput. Des. Eng. 5(1), 19–40 (2018)Google Scholar
  7. 7.
    Hirschberg, J., Manning, C.D.: Advances in natural language processing. Science 349(6245), 261–266 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kan, J., Gero, J.: Using the fbs ontology to capture semantic design information in design protocol studies. In: About: Designing-Analysing Design Meetings (2009)Google Scholar
  9. 9.
    Lloyd, P., McDonnell, J., Reid, F., Luck, R.: Dtrs7 dataset from 7th design thinking research symposium. http://design.open.ac.uk/dtrs7
  10. 10.
    Luck, R.: Does this compromise your design? socially producing a design concept in talk-in-interaction (2009)Google Scholar
  11. 11.
    McCowan, I., Carletta, J., Kraaij, W., Ashby, S., Bourban, S., Flynn, M., Guillemot, M., Hain, T., Kadlec, J., Karaiskos, V., et al.: The ami meeting corpus. In: Proceedings of the 5th International Conference on Methods and Techniques in Behavioral Research, vol. 88, p. 100 (2005)Google Scholar
  12. 12.
    McDonnell, J.: Collaborative negotiation in design: a study of design conversations between architect and building users. CoDesign 5(1), 35–50 (2009)CrossRefGoogle Scholar
  13. 13.
    McDonnell, J., Lloyd, P.: About: Designing-Analysing Design Meetings. CRC Press (2009)Google Scholar
  14. 14.
    McDonnell, J., Lloyd, P.: Beyond specification: A study of architect and client interaction. Des. Stud. 35(4), 327–352 (2014)CrossRefGoogle Scholar
  15. 15.
    Oak, A.: As you said to me i said to them: reported speech and the multi-vocal nature of collaborative design practice. Des. Stud. 34(1), 34–56 (2013)CrossRefGoogle Scholar
  16. 16.
    Pauwels, P., Zhang, S., Lee, Y.C.: Semantic web technologies in aec industry: a literature overview. Autom. Constr. 73, 145–165 (2017)CrossRefGoogle Scholar
  17. 17.
    Perry, M., Sanderson, D.: Coordinating joint design work: the role of communication and artefacts. Des. Stud. 19(3), 273–288 (1998)CrossRefGoogle Scholar
  18. 18.
    Ramos, J., et al.: Using tf-idf to determine word relevance in document queries. In: Proceedings of the first instructional conference on machine learning, vol. 242, pp. 133–142 (2003)Google Scholar
  19. 19.
    Ren, Z., Yang, F., Bouchlaghem, N., Anumba, C.: Multi-disciplinary collaborative building designa comparative study between multi-agent systems and multi-disciplinary optimisation approaches. Autom. Constr. 20(5), 537–549 (2011)CrossRefGoogle Scholar
  20. 20.
    Russell, S.J., Stuart, J.: Norvig. Artificial Intelligence: A Modern Approach. pp. 111–114 (2003)Google Scholar
  21. 21.
    Schultz, C.P., Amor, R., Lobb, B., Guesgen, H.W.: Qualitative design support for engineering and architecture. Adv. Eng. Inform. 23(1), 68–80 (2009)CrossRefGoogle Scholar
  22. 22.
    Silge, J., Robinson, D.: Tidytext: Text mining and analysis using tidy data principles in r. J. Open Source Software 1(3) (2016)CrossRefGoogle Scholar
  23. 23.
    Tixier, A.J.P., Hallowell, M.R., Rajagopalan, B., Bowman, D.: Automated content analysis for construction safety: a natural language processing system to extract precursors and outcomes from unstructured injury reports. Autom. Constr. 62, 45–56 (2016)CrossRefGoogle Scholar
  24. 24.
    Tur, G., De Mori, R.: Spoken Language Understanding: Systems for Extracting Semantic Information from Speech. John Wiley & Sons (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil Systems EngineeringInstitute of Civil Engineering, Technical University of BerlinBerlinGermany

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