Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-28T01:59:15.046Z Has data issue: false hasContentIssue false

A function–behavior–structure ontology of processes

Published online by Cambridge University Press:  19 September 2007

John S. Gero
Affiliation:
Krasnow Institute for Advanced Study and Volgenau School of Information Technology and Engineering, George Mason University, Fairfax, Virginia, USA
Udo Kannengiesser
Affiliation:
NICTA, Alexandria, Australia

Abstract

This paper presents how the function–behavior–structure (FBS) ontology can be used to represent processes despite its original focus on representing objects. The FBS ontology provides a uniform framework for classifying processes, and includes higher level semantics in their representation. We show that this ontology supports a situated view of processes based on a model of three interacting worlds. The situated FBS framework is then used to describe the situated design of processes.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bartlett, F.C. (1932). Remembering: A Study in Experimental and Social Psychology. Cambridge: Cambridge University Press [reprinted 1977].Google Scholar
Bickhard, M.H., & Campbell, R.L. (1996). Topologies of learning. New Ideas in Psychology 14(2), 111156.CrossRefGoogle Scholar
Chandler, A.D. (1962). Strategy and Structure. Cambridge, MA: MIT Press.Google Scholar
Chandrasekaran, B., Goel, A.K., & Iwasaki, Y. (1993). Functional representation as design rationale. IEEE Computer 26(1), 4856.Google Scholar
Chandrasekaran, B., & Josephson, J.R. (2000). Function in device representation. Engineering with Computers 16(3–4), 162177.CrossRefGoogle Scholar
Clancey, W.J. (1997). Situated Cognition: On Human Knowledge and Computer Representations. Cambridge: Cambridge University Press.Google Scholar
Clibbon, K., & Edmonds, E. (1996). Representing strategic design knowledge. Engineering Applications of Artificial Intelligence 9(4), 349357.CrossRefGoogle Scholar
Cross, N. (1994). Engineering Design Methods: Strategies for Product Design. Chichester: Wiley.Google Scholar
Cummings, S., & Wilson, D., Eds. (2003). Images of Strategy. Oxford: Blackwell.Google Scholar
de Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artificial Intelligence 24, 783.Google Scholar
Dewey, J. (1896). The reflex arc concept in psychology. Psychological Review 3, 357370.CrossRefGoogle Scholar
Gero, J.S. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine 11(4), 2636.Google Scholar
Gero, J.S. (1999). Constructive memory in design thinking. In Design Thinking Research Symposium: Design Representation (Goldschmidt, G., & Porter, W., Eds.), pp. 2935. 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), 361368.CrossRefGoogle Scholar
Gero, J.S., & Kannengiesser, U. (2004). The situated function–behaviour–structure framework. Design Studies 25(4), 373391.Google Scholar
Grabowski, H., Rude, S., & Grein, G., eds. (1998). Universal Design Theory. Aachen, Germany: Shaker Verlag.Google Scholar
Gruber, T.R. (1989). Automated knowledge acquisition for strategic knowledge. Machine Learning 4, 293336.Google Scholar
Haymaker, J., & Fischer, M. (2001). Challenges and Benefits of 4D Modeling on the Walt Disney Concert Hall Project, CIFE Working Paper 64. Stanford, CA: Center for Integrated Facility Engineering, Stanford University.Google Scholar
Hori, K. (2000). An ontology of strategic knowledge: key concepts and applications, Knowledge-Based Systems 13, 369374.Google Scholar
Hubka, V., & Eder, W.E. (1996). Design Science: Introduction to the Needs, Scope and Organization of Engineering Design Knowledge. Berlin: Springer–Verlag.Google Scholar
International Alliance for Interoperability. (2006). Industry Foundation Classes IFC2x (3rd ed.). Accessed at http://www.iai-international.org/Model/R2x3_final/index.htmGoogle Scholar
Kitamura, Y., Kashiwase, M., Fuse, M., & Mizoguchi, R. (2004). Deployment of an ontological framework of functional design knowledge. Advanced Engineering Informatics 18(2), 115127.CrossRefGoogle Scholar
Mintzberg, H., & Waters, J.A. (1985). Of strategies, deliberate and emergent. Strategic Management Journal 6(3), 257272.Google Scholar
Motus, L., & Rodd, M.G. (1994). Timing Analysis of Real-Time Software. Oxford: Pergamon Press.Google Scholar
Murdock, J.W., & Goel, A. (2001). Meta-case-based reasoning: using functional models to adapt case-based agents. Proc. Int. Conf. Case-Based Reasoning 2001 (Aha, D.W., & Watson, I., Eds.), pp. 407421. Berlin: Springer.Google Scholar
NIST. (1993). Integration Definition for Function Modeling (IDEF0), Federal Information Processing Standards Publication 183. Gaithersburg, MD: National Institute of Standards and Technology.Google Scholar
NIST. (2000). The Process Specification Language (PSL): Overview and Version 1.0 Specification, NIST Internal Report 6459. Gaithersburg, MD: National Institute of Standards and Technology.Google Scholar
NIST. (2004). Inputs and Outputs in the Process Specification Language, NIST Internal Report 7152. Gaithersburg, MD: National Institute of Standards and Technology.Google Scholar
Schön, D.A. (1983). The Reflective Practitioner: How Professionals Think in Action. New York: Harper Collins.Google Scholar
Schön, D.A., & Wiggins, G. (1992). Kinds of seeing and their functions in designing. Design Studies 13(2), 135156.Google Scholar
Sim, S.K., & Duffy, A.H.B. (1998). A foundation for machine learning in design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(2), 193209.Google Scholar
Smith, G.J., & Gero, J.S. (2005). What does an artificial design agent mean by being “situated”? Design Studies 26(5), 535561..CrossRefGoogle Scholar
Stone, R.B., & Wood, K.L. (2000). Development of a functional basis for design. Journal of Mechanical Design 122(4), 359370.Google Scholar
Stroulia, E., & Goel, A. (1995). Functional representation and reasoning in reflective systems. Journal of Applied Intelligence 9(1), 101124.Google 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. 297320. Sydney, Australia: University of Sydney, Key Centre of Design Computing and Cognition.Google Scholar
von der Weth, R. (1999). Design instinct?—The development of individual strategies. Design Studies 20(5), 453463.Google Scholar
Wiest, J.D., & Levy, F.K. (1977). A Management Guide to PERT/CPM. Englewood Cliffs, NJ: Prentice–Hall.Google 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. 177190. New York: Plenum Press.Google Scholar