Advertisement

People-Centered Visuospatial Cognition: Next-Generation Architectural Design Systems and Their Role in Design Conception, Computing, and Communication

  • Mehul Bhatt
  • Carl Schultz
Chapter
Part of the Philosophy of Engineering and Technology book series (POET, volume 28)

Abstract

When undertaking the task of design, architects imagine and anticipate the visuospatial and navigational experience of building users during the initial design conception phase. The ultimate goal is to ensure that the final physical built-up structure inherently performs with respect to people-centered design criteria encompassing function, behavior, and affordance. We argue that next-generation people-centered design systems, frameworks, assistive tools, educational discourse, and design policies and practices need to be explicitly founded on the cognitive modalities of human perception, attention, action, dynamics, environmental affordance and user experience, and design conception and semantics. We posit that this requires a holistic approach to architectural design cognition, encompassing the application of principles, practices, and methods from the fields of architecture and engineering, cognitive science, spatial cognition and computation, and evidence-based empirical methods in environmental and social psychology.

Keywords

Computer science Cognitive science Artificial intelligence Cognitive systems Human-computer interaction Architecture Design Social and behavioural sciences Spatial cognition and computation Spatial reasoning Design semantics People-centered design Spatial assistance systems Computer-aided Architecture Design (CAAD) 

References

  1. Akin, Ö. (1993). Architects’ reasoning with structures and functions. Environment and Planning B: Planning and Design, 20(3), 273–294.CrossRefGoogle Scholar
  2. Akin, Ö. (2011). Iteration: What is it good for? In M. Bhatt, C. Hoelscher, & T. Shipley (Eds.) Spatial Cognition for Architectural Design (SCAD 2011), November 2011, Spatial Cognition Research Center (SFB/TR 8) Report Series.Google Scholar
  3. Baldwin, C. (2007). Steps toward a science of design. NSF principal investigators conference on the science of design. http://www.people.hbs.edu/cbaldwin/DR2/BaldwinScienceofDesignSteps.pdf. Accessed 30 May 2017.
  4. Bayazit, N. (2004). Investigating design: A review of forty years of design research. Design Issues, 20(1).Google Scholar
  5. Bechtel, R., & Churchman, A. (2002). Handbook of environmental psychology. New York: Wiley.Google Scholar
  6. Bhatt, M. & Freksa, C. (2010). Spatial computing for design: An artificial intelligence perspective. In: US NSF International Workshop on Studying Visual and Spatial Reasoning for Design Creativity, Aix-en-Provence.Google Scholar
  7. Bhatt, M., Guesgen, H., Wölfl, S., & Hazarika, S. (2011a). Qualitative spatial and temporal reasoning: Emerging applications, trends, and directions. Spatial Cognition & Computation, 11(1), 1–14.CrossRefGoogle Scholar
  8. Bhatt, M., Hoelscher, C., & Shipley, T. (Eds.). 2011b). Spatial Cognition for Architectural Design (SCAD 2011), November 2011, Spatial Cognition Research Center (SFB/TR 8) Report Series.Google Scholar
  9. Bhatt, M., Hois, J., & Kutz, O. (2012a). Ontological modelling of form and function for architectural design. Applied Ontology Journal, 7(3), 233–267.Google Scholar
  10. Bhatt, M., Schultz, C., Huang, M. (2012b). The shape of empty space: Human-centered cognitive foundations in computing for spatial design. In VL/HCC 2012: IEEE Symposium on Visual Languages and Human-Centric Computing (pp. 33–40).Google Scholar
  11. Bhatt, M., Borrmann, A., Amor, R., & Beetz, J. (2013a). Architecture, computing, and design assistance. Automation in Construction, 32, 161–164.CrossRefGoogle Scholar
  12. Bhatt, M., Schultz, C., & Freksa, C. (2013b). The ‘Space’ in spatial assistance systems: Conception, formalisation and computation. In T. Tenbrink, J. Wiener, & C. Claramunt (Eds.), Representing space in cognition: Interrelations of behavior, language, and formal models (pp. 171–214). Oxford: Oxford University Press.Google Scholar
  13. Bhatt, M., Schultz, C., & Thosar, M. (2014). Computing narratives of cognitive user experience for building design analysis: Kr for industry scale computer-aided architecture design. In: T. Eiter, C. Baral, & G Giacomo (Eds.), Principles of knowledge representation and reasoning: Proceedings of the 14th International Conference, KR.Google Scholar
  14. Brown, D. (1993). Intelligent computer-aided design. In J. G. Williams & K. Sochats (Eds.), Encyclopedia of computer science and technology. New York: Dekker.Google Scholar
  15. Brown, D. (2007). AI EDAM at 20. AI EDAM: Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 21(1), 1–2.Google Scholar
  16. Chandrasekaran, B. (1990). Design problem solving: A task analysis. AI Magazine, 11(4), 59–71.Google Scholar
  17. Ching, F. (1979). Architecture: Form, space, and order. New York: VNR.Google Scholar
  18. DeFanti, T., Dawe, G., Sandin, D., Schulze, J., Otto, P., Girado, J., Kuester, F., Smarr, L., & Rao, R. (2009). The starCAVE, a third-generation CAVE and virtual reality OptIPortal. Future Generation Computer Systems, 25(2), 169–178.CrossRefGoogle Scholar
  19. Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2008). BIM Handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors. In Frontiers in artificial intelligence and applications. Hoboken: Wiley.Google Scholar
  20. Finlayson, M., Fisseni, B., Löwe, B., & Meister, J. C. (Eds.). 2013, August 4–6). Workshop on Computational Models of Narrative, CMN, Hamburg, Germany. OpenAccess Series in Informatics 32.Google Scholar
  21. Fisher, W. R. (1987). Human communication as narration: Toward a philosophy of reason, value, and action, Columbia, SC.Google Scholar
  22. Froese, T., Fischer, M., Grobler, F., Ritzenthaler, J., Yu, K., Sutherland, S., Staub, S., Akinci, B., Akbas, R., Koo, B., Barron, A., & Kunz, J. (1999). Industry foundation classes for project management – A trial implementation. Journal of Information Technology in Construction, 4, 17–36.Google Scholar
  23. Gaizauskas, R., Barker, E., Chang, C., Derczynski, L., Phiri, M., Peng, C. (2012). Applying ISO-Space to Healthcare Facility Design Evaluation Reports. In Proceedings of the Joint ISA-7, SRSL-3 and I2MRT Workshop on Semantic Annotation and the Integration and Interoperability of Multimodal Resources and Tools.Google Scholar
  24. Gero, J. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine, 11(4), 26–36.CrossRefGoogle Scholar
  25. Gero, J. (2007). AI EDAM at 20: Artificial intelligence in designing. AI EDAM: Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 21/1, 17–18.Google Scholar
  26. Gero, J., Tham, K., Lee, H. (1999): Behavior: A link between function and structure in design. In D. Brown, M. Waldron, H. Yoshikawa (Eds.), Intelligent Computer Aided Design, volume B-4 of IFIP Transactions (pp. 193–225). North-Holland.Google Scholar
  27. Goldschmidt, G. (2011). The black curtained studio: Eulogy to a dead pencil. In: M. Bhatt, C. Hoelscher and T. Shipley (Eds.), Spatial Cognition for Architectural Design (SCAD 2011), November 2011, Spatial Cognition Research Center (SFB/TR 8) Report Series.Google Scholar
  28. Herman, D., Jahn, M., & Ryan, M. L. (2005). Routledge Encyclopedia of narrative theory. London/New York: Routledge.Google Scholar
  29. Hirtz, J., Stone, R., McAdams, D., Szykman, S., & Wood, K. (2002). A functional basis for engineering design: Reconciling and evolving previous efforts. Research in Engineering Design, 13(2), 65–82.CrossRefGoogle Scholar
  30. Horwitz, J. & Singley, P. (Eds.). (2004). Eating architecture, Cambridge, MA: MIT Press.Google Scholar
  31. Krishnamurti, R. (2006). Explicit design space? Artificial intelligence. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 20(2), 95–103.CrossRefGoogle Scholar
  32. Loos, A. (1930). Ornament and crime. Innsbruck (reprint Vienna).Google Scholar
  33. Mani, I. (2012). Computational modeling of narrative. Synthesis Lectures on Human Language Technologies, 5(3), 1–142.CrossRefGoogle Scholar
  34. Mastrodonato, G., Bhatt, M., Schultz, C. (2013). Lost in rotation: Investigating the effects of landmarks and staircases on orientation. In 36th European Conference on Visual Perception. Google Scholar
  35. Preiser, W., Rabinowitz, H., & White, E. (1988). Post occupancy evaluation. New York: Van Nostrand Reinhold.Google Scholar
  36. Schultz, C., Bhatt, M. (2011). Toward accessing spatial structure from building information models. In 28th Urban Data Management Symposium (UDMS 2011), volume XXXVIII-4/C21. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial.Google Scholar
  37. Schultz, C. & Bhatt, M.. (2013a) InSpace3D: A middleware for built environment data access and analytics, in: Proceedings of the International Conference on Computational Science (ICCS 2013), in cooperation with SIGHPC (pp. 80–89), Barcelona, Spain.Google Scholar
  38. Schultz, C., & Bhatt, M. (2013b). InSpace3D: A middleware for built environment data access and analytics. In Proceedings of the International Conference on Computational Science (ICCS 2013), in cooperation with SIGHPC (pp. 80–89), Barcelona, Spain.Google Scholar
  39. Schultz, C., Bhatt, M., & Mora, R. (2013). MindYourSpace – A tool for evidence-based qualitative analyses of user experience and navigation behavior in the built environment. In edra44providence – 44th Environmental Design Research Association Conference. Google Scholar
  40. Sullivan, L. (1896). The tall office building artistically considered. Lippincott’s Magazine, 57, 403–409.Google Scholar
  41. Tostoes, A., Carapinha, A., & Corte-Real, P. (2006). Gulbenkian: Architecture and landscape. Lisbon: Calouste Gulbenkian Foundation.Google Scholar
  42. Umeda, Y., & Tomiyama, T. (1997). Functional reasoning in design. IEEE Expert: Intelligent Systems and Their Applications, 12, 42–48.CrossRefGoogle Scholar
  43. Umeda, Y., Takeda, H., Tomiyama, T., & Yoshikawa, H. (1990). Function, behavior and structure. In Applications of AI in Engineering (AIENG-90) (pp. 177–193). Southhampton.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The DesignSpace Group, Faculty of Mathematics and InformaticsUniversity of BremenBremenGermany

Personalised recommendations