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Structure, behavior, and function of complex systems: The structure, behavior, and function modeling language

Published online by Cambridge University Press:  16 December 2008

Ashok K. Goel
Affiliation:
Design Intelligence Laboratory, School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA
Spencer Rugaber
Affiliation:
Design Intelligence Laboratory, School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA
Swaroop Vattam
Affiliation:
Design Intelligence Laboratory, School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA

Abstract

Teleological modeling is fundamental to understanding and explaining many complex systems, especially engineered systems. Research on engineering design and problem solving has developed several ontologies for expressing teleology, for example, functional representation, function–behavior–structure, and structure–behavior–function (SBF). In this paper, we view SBF as a programming language. SBF models of engineering systems have been used in several computer programs for automated design and problem solving. The SBF language captures the expressive power of the earlier programs and provides a basis for interactive construction of SBF models. We provide a precise specification of the SBF language. We also describe an interactive model construction tool called SBFAuthor.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2009

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References

REFERENCES

Anthony, L., Regli, W., John, J., & Lombeyda, S. (2001). An approach to capturing structure, behavior and function of artifacts in CAD. Journal of Computing and Information Science in Engineering 1 ( 2), 186192.CrossRefGoogle Scholar
Bhatta, S., & Goel, A. (1994). Model-based discovery of physical principles from design experiences. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8 ( 2), 113123.CrossRefGoogle Scholar
Bhatta, S., & Goel, A. (1997). Learning generic mechanisms for innovative design adaptation. Journal of Learning Sciences 6 ( 4), 367396.CrossRefGoogle Scholar
Buckley, B. (2000). Interactive multimedia and model-based learning in science education. International Journal of Science Education 22, 895935.CrossRefGoogle Scholar
Bunge, M. (1977). Treatise on Basic Philosophy. Volume 3: Ontology I: The Furniture of the World. Berlin: Springer.CrossRefGoogle Scholar
Bylander, T. (1991). A theory of consolidation for reasoning about devices. International Journal of Man–Machine Studies 35, 467489.CrossRefGoogle Scholar
Chakrabarti, A., & Bligh, T. (1996). Approach to functional synthesis of mechanical design concepts: theory, applications, and emerging research issues. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10 ( 4), 313331.CrossRefGoogle Scholar
Chandrasekaran, B. (1994). Functional representation: a brief historical perspective. Applied Artificial Intelligence 8 ( 2), 173197.CrossRefGoogle Scholar
Chandrasekaran, B., Goel, A., & Iwasaki, Y. (1993). Functional representation as a basis for design rationale. IEEE Computer 26(1), 4856.CrossRefGoogle Scholar
Chandrasekaran, B., & Josephson, J. (2000). Function in device representation. Engineering with Computers 16, 162177.CrossRefGoogle Scholar
Clement, J. (1989). Learning via model construction and criticism: protocol evidence on sources on creativity in science. In Handbook of Creativity: Assessment, Theory and Research (Glover, J.A., Ronning, R.R., & Reynolds, C.R., Eds.), pp. 341381. New York: Plenum.CrossRefGoogle Scholar
Clement, J. (2000). Model-based learning as a key research area in science education. International Journal of Science Education 22, 10411053.CrossRefGoogle Scholar
Erden, M., Komoto, H., van Beek, T., D'Amelio, V., Echavarria, E., & Tomiyama, T. (2008). A review of function modeling: approaches and applications. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22 ( 2), 147169.CrossRefGoogle Scholar
Gero, J. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11 ( 4), 2636.Google Scholar
Gero, J.S., Tham, K.W., & Lee, H.S. (1992). Behavior: a link between function and structure in design. In Intelligent Computer Aided Design (Brown, D.C., Waldron, M.B., & Yoshikawa, H., Eds.), pp. 193225. Amsterdam: North–Holland.Google Scholar
Goel, A. (1992). Representation of design functions in experience-based design. In Intelligent Computer Aided Design (Brown, D.C., Waldron, M.B., & Yoshikawa, H., Eds.), pp. 283308. Amsterdam: North–Holland.Google Scholar
Goel, A., & Bhatta, S. (2004). Design patterns: a unit of analogical transfer in creative design. Advanced Engineering Informatics 18 ( 2), 8594.CrossRefGoogle Scholar
Goel, A., Bhatta, S., & Stroulia, E. (1997). Kritik: an early case-based design system. In Issues and Applications of Case-Based Reasoning in Design (Maher, M., & Pu, P., Eds.), pp. 87132. Mahwah, NJ: Erlbaum.Google Scholar
Goel, A., & Chandrasekaran, B. (1989). Functional representation of designs and redesign problem solving. Proc. 11th Int. Joint Conf. Artificial Intelligence (IJCAI-89), pp. 13881394. Los Altos, CA: Morgan Kaufmann.Google Scholar
Goel, A., & Chandrasekaran, B. (1992). Case-based design: a task analysis. In Artificial Intelligence Approaches to Engineering Design. Volume II: Innovative Design (Tong, C., & Sriram, D., Eds.), pp. 165184. San Diego, CA: Academic Press.CrossRefGoogle Scholar
Goel, A., Gomez, A., Grue, N., Murdock, W., Recker, M., & Govindaraj, T. (1996). Towards design learning environments—explaining how devices work. Proc. Int. Conf. Intelligent Tutoring SystemsMontreal.Google Scholar
Goel, A., & Murdock, J. (1996). Meta-cases: explaining case-based reasoning. In Advances in Case-Based Reasoning. Lecture Notes in Artificial Intelligence (Smith, I., & Faltings, B., Eds.), Vol. 1168. Berlin: Springer–Verlag.Google Scholar
Griffith, T., Nersessian, N., & Goel, A. (2000). Function-follows-form transformations in scientific problem solving. Proc. 22nd Annual Conf. Cognitive Science Society, pp. 196210. Mahwah, NJ: Erlbaum.Google Scholar
Hmelo, C., Holton, D., & Kolodner, J. (2000). Designing to learn about complex systems. Journal of the Learning Sciences 9 ( 3), 247298.CrossRefGoogle Scholar
Hmelo-Silver, C., Jordan, R., Liu, L., Gray, S., Demeter, M., Rugaber, S., Vattam, S., & Goel, A. (2008). Focusing on function: thinking below the surface of complex natural systems. Science Scope 2735.Google Scholar
Hmelo-Silver, C., & Pfeffer, M. (2004). Comparing expert and novice understanding of a complex systems from the perspectives of structures, behaviors and functions. Cognitive Science 28, 127138.CrossRefGoogle Scholar
International Organization for Standardization and International Electrotechnical Commission. (1996). Information Technology–Syntactic Metalanguage–Extended BNF, 1st ed. ISO Rep. 1996-l 2-l 5, ISO/IEC 14977: 1996(E). Accessed at http://standards.iso.org/ittf/PubliclyAvailableStandards/s026153_ISO_IEC_14977_1996(E).zipGoogle 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
Nosek, J., & Roth, I. (1990). A comparison of formal knowledge representation schemes as communication tools: predicate logic vs. semantic network. International Journal of Man–Machine Studies 33, 227239.CrossRefGoogle Scholar
Novak, J., & Gowin, D. (1984). Learning How to Learn. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Prabhakar, S., & Goel, A. (1998). Functional modeling for enabling adaptive design of devices for new environments. Artificial Intelligence in Engineering 12, 417444.CrossRefGoogle Scholar
Rasmussen, J. (1985). The role of hierarchical knowledge representation in decision making and system management. IEEE Transactions on Systems, Man, and Cybernetics 15, 234243.CrossRefGoogle Scholar
Rasmussen, J., Pejterson, A., & Goodstein, L. (1994). Cognitive Systems Engineering. New York: Wiley.Google Scholar
Sasajima, M., Kitamura, Y., Ikeda, M., & Mizoguchi, R. (1995). FBRL: a function and behavior representation language. Proc. IJCAI-95, pp. 18301836.Google Scholar
Sembugamoorthy, V., & Chandrasekaran, B. (1986). Functional representation of devices and compilation of diagnostic problem-solving systems. In Experience, Memory, and Reasoning (Kolodner, J.L., & Riesbeck, C.K., Eds.), pp. 4773. Mahwah, NJ: Erlbaum.Google Scholar
Simon, H. (1996). Sciences of the Artificial, 3rd ed.Boston: MIT Press.Google Scholar
Stone, R., & Wood, K. (2000). Development of a functional basis for design. Journal of Mechanical Design 122 ( 4), 359370.CrossRefGoogle Scholar
Szykman, S., Racz, J., Bochenek, C., & Sriram, R. (2000). A web-based system for design artifact modeling. Design Studies 21 ( 2), 145164.CrossRefGoogle Scholar
Szykman, S., Sriram, R., Bochenek, C., Racz, J., & Senfaute, J. (2000). Design repositories: engineering design's new knowledge base. IEEE Intelligent Systems 15 ( 3), 4855.CrossRefGoogle Scholar
Umeda, Y., Takeda, H., Tomiyama, T., & Yoshikawa, H. (1990). Function, behavior, and structure. Proc. AIENG '90 Applications of AI in Engineering (pp. 177193). Southerton/Berlin: Computational Mechanics Publications/Springer–Verlag.Google Scholar
Umeda, Y., Ishii, M., Yoshioka, M., Shimomura, Y., & Tomiyama, T. (1996). Supporting conceptual design based on the function–behavior–state modeler. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, 275288.CrossRefGoogle Scholar
Umeda, Y., & Tomiyama, T. (1997). Functional reasoning in design. IEEE Expert 12 ( 2), 4248.CrossRefGoogle Scholar
Vattam, S., Helms, M., & Goel, A. (2008). Compound analogical design: interaction between problem decomposition and analogical transfer in biologically inspired design. Proc. Third Int. Conf. Design Computing and Cognition (Gero, J., & Goel, A., Eds.), pp. 377396. Berlin: Springer.CrossRefGoogle Scholar
Wilensky, U. (1999). NetLogo Itself: 1999. Evanston, IL: Northwestern University, Center for Connected Learning and Computer-Based Modeling. Accessed at http://ccl.northwestern.edu/netlogo/.Google Scholar
Yaner, P., & Goel, A. (2007). Understanding drawings by compositional analogy. Proc. 20th Int. Joint Conf. Artificial Intelligence (IJCAI-07), pp. 11311137, Hyderabad, India.Google Scholar
Yaner, P., & Goel, A. (2008). Analogical recognition of shape and structure in design drawings. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22 ( 2), 117128.CrossRefGoogle Scholar