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Multimodal spatial data access for architecture design assistance

Published online by Cambridge University Press:  20 April 2012

Carl Schultz  and
Mehul Bhatt
Carl Schultz*
Affiliation:
Project [DesignSpace], University of Bremen, Bremen, Germany
Mehul Bhatt
Affiliation:
Project [DesignSpace], University of Bremen, Bremen, Germany
*
Reprint requests to: Carl Schultz, Project [DesignSpace], Spatial Cognition Research Center (SFB/TR 8), University of Bremen, Bibliothekstraße 1, Bremen 28359, Germany. E-mail: cschultz@informatik.uni-bremen.de
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Abstract

We present a multimodal spatial data access framework designed to serve the informational and computational requirements of architectural design assistance systems that are intended to provide intelligent spatial decision support and analytical capabilities. The framework focuses on multiperspective semantics, qualitative and artifactual spatial abstractions, and industrial conformance and interoperability within the context of the industry foundation classes. The framework provides qualitative and cognitively adequate representational mechanisms, and the formal interpretation of the structural form of indoor spaces that are not directly provided by conventional computer-aided design based or quantitative models of space. We illustrate the manner in which these representations directly provide the spatial abstractions that are needed to enable the implementation of intelligent analytical capabilities in design assistance tools. We introduce the framework, and also provide detailed use cases that illustrate the usability of the framework and the manner of its utilization within architectural design assistance systems.

Keywords

Decision Support for Design ArchitectureDesign SemanticsIntelligent Computer-Aided Architectural DesignKnowledge Representation and ReasoningQualitative Spatial Representation and ReasoningSpatial Computing for Design
Type
Special Issue Articles
Information
AI EDAM , Volume 26 , Issue 2: Design Computing and Cognition (DCC'10) , May 2012 , pp. 177 - 203
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Benedikt, M.L. (1979). To take hold of space: isovists and isovist fields. Environment and Planning B: Planning and Design 6(1), 47–65.CrossRefGoogle Scholar
Bhatt, M., Dylla, F., & Hois, J. (2009). Spatio-terminological inference for the design of ambient environments. Conf. Spatial Information Theory (COSIT'09) (Hornsby, K.S., Claramunt, C., Denis, M., & Ligozat, G., Eds.), pp. 371–391. Berlin: Springer–Verlag.CrossRefGoogle Scholar
Bhatt, M., & Freksa, C. (2010). Spatial computing for design: an artificial intelligence perspective. Proc. NSF Int. Workshop on Studying Visual and Spatial Reasoning for Design Creativity (SDC'10).Google Scholar
Bhatt, M., Hois, J., & Kutz, O. (2011). Ontological modelling of form and function for architectural design. Applied Ontology 2011, 1–31.Google Scholar
Bhatt, M., Lee, J.H., & Schultz, C. (2011). CLP(QS): a declarative spatial reasoning framework. Conf. Spatial Information Theory (COSIT), pp. 210–230.CrossRefGoogle Scholar
Bhatt, M., Schultz, C., & Freksa, C. (2012). The “space” in spatial assistance systems: conception, formalisation and computation. In Representing Space in Cognition: Interrelations of Behavior, Language, and Formal Models. Series: Explorations in Language and Space (Tenbrink, T., Wiener, J., & Claramunt, C., Eds.), New York: Oxford University Press.Google Scholar
Cohn, A., & Hazarika, S. (2001). Qualitative spatial representation and reasoning: an overview. Fundamental Information 46(1–2), 1–29.Google Scholar
Cohn, A.G., Bennett, B., Gooday, J., & Gotts, N.M. (1997). Qualitative spatial representation and reasoning with the region connection calculus. Geoinformatica 1(3), 275–316.CrossRefGoogle Scholar
Cohn, A.G., & Renz, J. (2007). Qualitative spatial reasoning. In Handbook of Knowledge Representation (van Harmelen, F., Lifschitz, V., & Porter, B., Eds.). New York: Elsevier.Google Scholar
Cuttle, C. (2003). Lighting by Design. Amsterdam: Elsevier.Google Scholar
de Berg, M., van Kreveld, M., Overmars, M., & Schwarzkopf, O. (2000). Computational Geometry: Algorithms and Applications, 2nd ed.Berlin: Springer–Verlag.CrossRefGoogle Scholar
Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2008). BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors. Frontiers in Artificial Intelligence and Applications. New York: Wiley.CrossRefGoogle Scholar
Flynn, J.E. (1977). A study of subjective responses to low energy and nonuniform lighting systems. Lighting Design and Application 7(2), 6–15.Google Scholar
Flynn, J.E., Spencer, T.J., Martyniuk, O., & Hendrick, C. (1973). Interim study of procedures for investigating the effect of light on impression and behaviour. Journal of the Illuminating Engineering Society 3( 2), 87–94.CrossRefGoogle Scholar
Freksa, C. (1991). Qualitative spatial reasoning. In Cognitive and Linguistic Aspects of Geographic Space (Mark, D., & Frank, A., Eds.), pp. 361–372. Dordrecht: Kluwer.CrossRefGoogle Scholar
Freksa, C. (1992). Using orientation information for qualitative spatial reasoning. Proc. Int. Conf. GIS, From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, pp. 162–178. Berlin: Springer–Verlag.CrossRefGoogle Scholar
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. Proc. ITCon 4, pp. 17–36.Google Scholar
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Gero, J.S., Tham, K.W., & Lee, H.S. (1991). Behavior: a link between function and structure in design. Proc. IntCAD, pp. 193–225.Google Scholar
Kowadlo, G., & Russell, R.A. (2006). Using naïve physics for odor localization in a cluttered indoor environment. Autonomous Robots 20(3), 215–230.CrossRefGoogle Scholar
Li, X., Claramunt, C., & Ray, C. (2009). A continuous-based representation for the analysis of indoor spaces. Proc. STAMI, pp. 44–53.Google Scholar
Ligozat, G., Mitra, D., & Condotta, J.-F. (2004). Spatial and temporal reasoning: beyond Allen's calculus. AI Communications 17(4), 223–233.Google Scholar
Lozano-Pérez, T., & Wesley, M.A. (1979). An algorithm for planning collision-free paths among polyhedral obstacles. Communications in ACM 22(10), 560–570.CrossRefGoogle Scholar
Moratz, R. (2006). Representing relative direction as a binary relation of oriented points. Proc. ECAI, pp. 407–411.Google Scholar
Randell, D.A., Cui, Z., & Cohn, A. (1992). A spatial logic based on regions and connection. Proc. KR'92. Principles of Knowledge Representation and Reasoning, pp. 165–176. San Mateo, CA: Morgan Kaufmann.Google Scholar
Renz, J., & Nebel, B. (2007). Qualitative spatial reasoning using constraint calculi. In Handbook of Spatial Logics (Aiello, M., Pratt-Harmann, I., & Benthem, J.v., Eds.), pp. 161–215. New York: Springer.CrossRefGoogle Scholar
Tostoes, A., Carapinha, A., & Corte-Real, P. (2006). Gulbenkian: Architecture and Landscape. Lisbon, Portugal: Calouste Gulbenkian Foundation.Google Scholar
Werner, S., Krieg-Brückner, B., & Herrmann, T. (2000). Modelling navigational knowledge by route graphs. In Spatial Cognition II, Integrating Abstract Theories, Empirical Studies, Formal Methods, and Practical Applications (Freksa, C., Brauer, W., Habel, C., & Wender, K.F., Eds.), pp. 295–316. London: Springer–Verlag.CrossRefGoogle Scholar