Approximate Enclosed Space Using Virtual Agent

  • Aswin Indraprastha
  • Michihiko Shinozaki


In agent-based pedestrian model, steering movement is driven by position of attractor or goal, and a graph of their relations. Our work studied on constructing relationship between spatial cognition and enclosed space using virtual agent. Instead of focusing on location-based goals, we are investigating enclosed space as primary factor for locomotion. Our contribution on the identification of enclosure enhances the artificial model of spatial cognition. This is significant for the development of agent-based simulation with spatial cognition to determine and to measure space in architectural design model. We present our approach using three stages of methods. First, we constructed object detection algorithm on agent line of sight. Second, by decomposing detected objects as set of points, we analyzed their attributes and properties to define center of enclosed space. Third, as point of enclosed spaces determined, we classified them into L-shaped space and U-shaped space using simple arithmetic algorithm. Finally, computational results of points represent goals for navigational purpose.


Spatial Cognition Virtual Agent Architectural Element Joint Point Enclose Space 
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.
    Lawson, B.: The Language of Space, 5th edn. Architectural Press, Elsevier (2007)Google Scholar
  2. 2.
    Rasmussen, S.E.: Experiencing Architecture. MIT Press, Cambridge (1959)Google Scholar
  3. 3.
    March, L., Steadman, P.: The Geometry of Environment. MIT Press, Cambridge (1971)Google Scholar
  4. 4.
    Golledge, G., Reginald, R., Stimson, J.: Spatial Behavior: A Geographic Perspective. Guilford Publication, New York (1997)Google Scholar
  5. 5.
    Ching, F.D.K.: Architecture: Form, Space and Order, 3rd edn. John Wiley and Sons, New Jersey (2007)Google Scholar
  6. 6.
    Wiener, J.M., Franz, G.: Isovists as a Means to Predict Spatial Experience and Behavior. In: Freksa, C., Knauff, M., Krieg-Brückner, B., Nebel, B., Barkowsky, T. (eds.) Spatial Cognition IV. LNCS (LNAI), vol. 3343, pp. 42–57. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Turner, A., Penn, A.: Encoding Natural Movement As an Agent-based System: An Investigation into Human Pedestrian Behavior in The Built Environment. Journal of Environment and Planning B: Planning and Design 29, 473–490 (2002)CrossRefGoogle Scholar
  8. 8.
    Mankyu, S., Gleicher, M., Chenney, S.: Scalable Behaviors for Crowd Simulation, Eurographic. Blackwell Publishing, Oxford (2004)Google Scholar
  9. 9.
    Thalmann, D., Musse, S.R., Kallman, M.: From Individual Human Agent to Crowds, Informatik (2000)Google Scholar
  10. 10.
    Kallman, M., Thalmann, D.: Modeling Behaviors of Interactive Objects for Real Time Virtual Environment, Computer Graphics Lab, Swiss Federal Institute of Technology (2004)Google Scholar
  11. 11.
    Narahara, T.: Enactment Software: Spatial Designs Using Agent-based Models. Complex Interaction and Social Emergence (2007)Google Scholar
  12. 12.
    Wei, Y., Kalay, Y.E.: Geometric, Cognitive and Behavioral Modeling of Environmental Users. In: Design Computing and Cognition 2006. Springer, Heidelberg (2006)Google Scholar
  13. 13.
    Needham, T.: Visual Complex Analysis. Clarendon Press, Oxford (1997)zbMATHGoogle Scholar
  14. 14.
    Graham, R.L.: An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set. Information Processing Letters 1, 132–133 (1972)zbMATHCrossRefGoogle Scholar

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© Springer Netherlands 2011

Authors and Affiliations

  • Aswin Indraprastha
    • 1
  • Michihiko Shinozaki
    • 1
  1. 1.Shibaura Institute of TechnologyJapan

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