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An ontology of situated design teams

Published online by Cambridge University Press:  06 August 2007

John S. Gero  and
Udo Kannengiesser
John S. Gero
Affiliation:
Krasnow Institute for Advanced Study and Department of Computer Science, George Mason University, Fairfax, Virginia, USA
Udo Kannengiesser
Affiliation:
NICTA, Alexandria, Australia
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Abstract

This paper presents an ontological framework for situated design teams in which the team is both the subject and the object of designing. Team designing is modeled using the set of processes provided by the situated function–behavior–structure framework. This is a formal basis for understanding the drivers for change in the product to be designed and in the design team. We specifically focus on changes in a team's structure that emerge from interactions among individual team members and subteams.

Keywords

Design TeamsFunction–Behavior–Structure FrameworkSituatednessTeam Interaction
Type
Research Article
Information
AI EDAM , Volume 21 , Issue 3 , August 2007 , pp. 295 - 308
Copyright
Copyright © Cambridge University Press 2007

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References

REFERENCES

Bartlett, F.C. (1932, reprinted in 1977). Remembering: A Study in Experimental and Social Psychology. Cambridge: Cambridge University Press.Google Scholar
Bickhard, M.H., & Campbell, R.L. (1996). Topologies of learning. New Ideas in Psychology 14(2), 111–156.CrossRefGoogle Scholar
Bradley, J.H., & Hebert, F.J. (1997). The effect of personality type on team performance. Journal of Management Development 16(5), 337–353.CrossRefGoogle Scholar
Brown, D.C. (1998). Defining configuring. AIEDAM: Artificial Intelligence for Engineering, Design, and Manufacturing 12(4), 301–305.CrossRefGoogle Scholar
Clancey, W.J. (1997). Situated Cognition: On Human Knowledge and Computer Representations. Cambridge: Cambridge University Press.Google Scholar
Clark, H.H. (1996). Using Language. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Cohen, P.R., & Levesque, H.J. (1991). Teamwork. Noûs 25(4), 487–512.CrossRefGoogle Scholar
Corkill, D.D., & Lesser, V.R. (1983). The use of meta-level control for coordination in a distributed problem-solving network. In Int. Joint Conf. Artificial Intelligence ‘83 Bond, A.H., & Gasser, L., Eds.), pp. 748–755, Karlsruhe, Germany.Google Scholar
de Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artificial Intelligence 24, 7–83.CrossRefGoogle Scholar
Dewey, J. (1981). The reflex arc concept in psychology. Psychological Review 3, 357–370.Google Scholar
Fischer, G. (2001). External and sharable artifacts as opportunities for social creativity in communities of interest. In Computational and Cognitive Models of Creative Design V (Gero, J.S. & Maher, M.L., Eds.), pp. 67–89. Sydney, Australia: Key Centre of Design Computing and Cognition, University of Sydney.Google Scholar
Galbraith, J.R. (1977). Organization Design: An Information Processing View. Reading, MA: Addison–Wesley.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. (1999). Constructive memory in design thinking. In Design Thinking Research Symp.: Design Representation (Goldschmidt, G. & Porter, W., Eds.), pp. 29–35. Cambridge, MA: MIT.Google Scholar
Gero, J.S., & Fujii, H. (2000). A computational framework for concept formation for a situated design agent. Knowledge-Based Systems 13(6), 361–368.CrossRefGoogle Scholar
Gero, J.S., & Kannengiesser, U. (2004 a). The situated function–behaviour–structure framework. Design Studies 25(4), 373–391.CrossRefGoogle Scholar
Gero, J.S., & Kannengiesser, U. (2004 b). Modelling expertise of temporary design teams. Journal of Design Research 4(2).CrossRefGoogle Scholar
Kannengiesser, U., & Gero, J.S. (2007). Agent-based interoperability without product model standards. Computer-Aided Civil and Infrastructure Engineering 22(2), 80–97.CrossRefGoogle Scholar
Kramer, R.M., & Tyler, T.R., Eds. (1996). Trust in Organizations: Frontiers of Theory and Research. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate peripheral participation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Levitt, R.E., Cohen, G.P., Kunz, J.C., Nass, C.I., Christiansen, T.R., & Jin, Y. (1994). The virtual design team: simulating how organization structure and information processing tools affect team performance. In Computational Organization Theory (Carley, K.M. & Prietula, M.J., Eds.), pp. 1–18. Hillsdale, NJ: Erlbaum.Google Scholar
Malone, T.W. (1987). Modeling coordination in organizations and markets. Management Science 33(10), 1317–1332.CrossRefGoogle Scholar
Mesarović, M.D., Macko, D., & Takahara, Y. (1970). Theory of Hierarchical, Multilevel, Systems. New York: Academic Press.Google Scholar
Minsky, M. (1985). The Society of Mind. New York: Simon & Schuster.Google Scholar
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science 5(1), 14–37.CrossRefGoogle Scholar
Schön, D.A., & Wiggins, G. (1992). Kinds of seeing and their functions in designing. Design Studies 13(2), 135–156.CrossRefGoogle Scholar
Smith, G.J., & Gero, J.S. (2005). What does an artificial design agent mean by being “situated”? Design Studies 26(5), 535–561.CrossRefGoogle Scholar
Sosa, R., & Gero, J.S. (2005). A computational study of creativity in design: The role of society. AIEDAM: Artificial Intelligence for Engineering, Design, and Manufacturing 19(4), 229–244.CrossRefGoogle Scholar
Suwa, M., Gero, J.S., & Purcell, T. (1999). Unexpected discoveries and s-inventions of design requirements: a key to creative designs. In Computational Models of Creative Design IV (Gero, J.S. & Maher, M.L., Eds.), pp. 297–320. Sydney, Australia: Key Centre of Design Computing and Cognition, University of Sydney.Google Scholar
Weber, M. (1968). Economy and Society: An Outline of Interpretive Sociology. New York: Bedminster Press.Google Scholar
Wegner, D.M. (1986). Transactive memory: a contemporary analysis of the group mind. In Theories of Group Behavior (Mullen, B. & Goethals, G.R., Eds.), pp. 185–208. New York: Springer–Verlag.Google Scholar
Wenger, E. (1998). Communities of Practice: Learning, Meaning and Identity. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Ziemke, T. (1999). Rethinking grounding. In Understanding Representation in the Cognitive Sciences: Does Representation Need Reality? (Riegler, A., Peschl, M. & von Stein, A., Eds.), pp. 177–190. New York: Plenum Press.CrossRefGoogle Scholar