Hostname: page-component-546b4f848f-hhr79 Total loading time: 0 Render date: 2023-06-04T17:00:52.550Z Has data issue: false Feature Flags: { "useRatesEcommerce": true } hasContentIssue false

Functional device models and model-Based diagnosis in adaptive design

Published online by Cambridge University Press:  27 February 2009

Ashok K. Goel
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332-0280, U.S.A.
Eleni Stroulia
Center for Applied Knowledge Processing, Helmholtzstr. 16, 89081 Ulm, Germany


We analyze the diagnosis task in the context of adaptive design and redesign of physical devices. We identify three types of diagnosis tasks that differ in the types of information they take as input: the design does not achieve a desired function of the device, the design results in an undesirable behavior, and a specific structural element in the design misbehaves. We describe a model-based approach for solving the diagnosis task in the context of adaptive design and redesign. This approach uses functional models that explicitly represent the device functions and use them to organize teleological and causal knowledge about the device. In particular, we describe a specific kind of functional model called structure—behavior—function (SBF) models in which the causal behaviors of the device are specified in terms of flow of substances through components. We illustrate the use of SBF models with three examples from Kritik2, a knowledge system that designs new devices by retrieving, diagnosing, and adapting old device designs.

Copyright © Cambridge University Press 1996

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.)



Abu-Hanna, A., Benjamins, R., & Jansweijer, W. (1992). Device understanding and modeling for diagnosis. IEEE Expert 6(2), 2632.CrossRefGoogle Scholar
Bhatta, S., Goel, A., & Prabhakar, S. (1994). Innovation in analogical design: A model-based approach. Proc. Third Int. Conf. Artif. Intell Design, pp. 5774. Kluwer Academic, Boston.Google Scholar
Bylander, T. (1991). A theory of consolidation for reasoning about devices. Man-Machine Stud. 35, 467489.CrossRefGoogle Scholar
Chandrasekaran, B. (1994). Functional representation and causal processes. In Advances in Computers, (Yovits, M., Ed.), pp. 73143. Academic Press, New York.Google Scholar
Chandrasekaran, B., Goel, A., & Iwasaki, Y. (1995). Functional representation as design rationale. IEEE Computer 4856.Google Scholar
Chittaro, L., Guida, G.Tasso, C., & Tappano, E. (1993). Functional and telelogical knowledge in the multimodeling approach for reasoning about physical systems. IEEE Trans. Systems, Man and Cybern. 23(6), 17181751.CrossRefGoogle Scholar
Davis, R. (1984). Diagnostic reasoning based on structure and behavior. Artif. Intell. 24, 347410.CrossRefGoogle Scholar
de Kleer, J. (1984). How circuits work. Artif. Intell. 24, 205280.CrossRefGoogle Scholar
Goel, A. (1991). A model-based approach to case adaptation. Proc. Thirteenth Annu. Conf. Cognitive Science Soc. pp. 143148.Google Scholar
Goel, A. (1992). Representation of design functions in experience-based design. In Intelligent Computer Aided Design, (Brown, D., Waldron, M. and Yoshikawa, H., Eds.), pp. 283308. North-Holland, Amsterdam.Google Scholar
Goel, A., & Chandrasekaran, B. (1989). Functional representation of designs and redesign problem solving. Proc. Eleventh Int. Joint Conf. Artif. Intell, pp. 13881394. Morgan Kauffman, Los Altos, CA.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. and Sriram, D., Eds.), pp. 165184. Academic Press, San Diego.CrossRefGoogle Scholar
Goel, A., Gomez, A., Pittges, J., Shankar, M., & Stroulia, E. (1994). A modelbased approach to redesign. Proc. Thirteenth SPIE Knowl. Based Systems. Conf., pp. 164171.Google Scholar
Goel, A., & Prabhakar, S. (1994). A control architecture for redesign verification. Proc. 1994 Australian—New Zealand Intell. Inf. Sys. Conf. Brisbane, Australia, 377381.Google Scholar
Govindaraj, T. (1987). Qualitative approximation methodology for modeling and simulation of large dynamic systems: Applications to a marine power plant. IEEE Transact. Sys. Man and Cybern. SMC-17(6), 937955.CrossRefGoogle Scholar
Hawkins, R., Sticklen, J., McDowell, J., Hill, T., & Boyer, R. (1994). Function-based modeling and troubleshooting. J. Applied Intell. 8(2), 285302.CrossRefGoogle Scholar
Hunt, J., Pugh, D., & Price, C. (1995). Failure mode effects analysis: A practical application of functional modeling. J. Applied Intell. 9(1), 3344.CrossRefGoogle Scholar
Iwasaki, Y., & Chandrasekaran, B. (1992). Design verification through function- and behavior-oriented representation: Bridging the gap between function and behavior. Proc. Second Int. Conf. Artif. Intell. pp. 597616. Kluwer Academic, Boston.Google Scholar
Iwasaki, Y., Fikes, R., Vescovi, M., & Chandrasekaran, B. (1993). How things are intended to work: Capturing functional knowledge in device design. Proc. 13th Int. Joint Conf. on Al, pp. 15161522, Morgan Kauffman, Los Altos, CA.Google Scholar
Keller, R., Manago, C., Nayak, P., & Rua, M. (1988). Large multi-use knowledge bases. Internal Memo, Knowledge Systems Laboratory, Stanford University.Google Scholar
Kumar, A., & Upadhyaya, S. (1995). Function-based discrimination during model-based diagnosis. J. Applied Intell 9(1), 6580.CrossRefGoogle Scholar
Larsson, J. (1996). Diagnosis based on explicit means-ends models. Artif. Intell. 80, 2993.CrossRefGoogle Scholar
Lind, M. (1994). Modeling goals and functions of complex industrial plants. J. Applied Intell. 8(2), 259283.CrossRefGoogle Scholar
Luk, K., Stroulia, E., & Goel, A. (1995). A model-based approach to reasoning about cycles and fields. Proc. IJCA1–93 Workshop on AI in Design, Chambern, France (August, 1993), 3641.Google Scholar
Navinchandra, D., Sycara, K., & Narasimhan, S. (1991). Behavioral synthesis in CADET, a case-based design tool. Proc. Seventh IEEE Conf. Artif. Intell. Applications, pp. 217221.CrossRefGoogle Scholar
Rieger, C., & Grinberg, M. (1980). A system for cause-effect representation and simulation for computer-aided design. In Artificial Intelligence and Pattern Recognition in Computer-Aided Design, (Latombe, J., Ed.), pp. 299334. North Holland, Amsterdam, Netherlands.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. and Riesbeck, C., Eds.), pp. 4773. Erlbaum, Hillsdale, NJ.Google Scholar
Stroulia, E., & Goel, A. (1992). Generic teleological mechanisms and their use in case adaptation. Proc. Fourteenth Annual Conf. of the Cognitive Science Soc., 319324.Google Scholar
Stroulia, E., & Goel, A. (1994). A model-based approach to reflective learning. Proc. 1994 European Conf. Machine Leant., 287306.Google Scholar
Stroulia, E., & Goel, A. (1995). Functional representation and reasoning in reflective systems. J. of Applied Intell. 9(1), 101124.CrossRefGoogle Scholar
Stroulia, E., Shankar, M., Goel, A., & Penberthy, L. (1992). A modelbased approach to blame assignment in design. Proc. Second Int. Conf. on AI in Design, pp. 519538. Kluwer Academic Press, Boston.Google Scholar
Sun, K., & Faltings, B. (1994). Supporting creative mechanical design. Proc. Third Int. Conf. Artif. Intell. Design, Kluwer Academic, Boston.Google Scholar
Umeda, Y., Takeda, H., Tomiyama, T., & Yoshikawa, H. (1990). Function, behavior and structure. In Al in Engineering, (Gero, J., Ed.), pp. 177193. Springer-Verlag, Berlin.Google Scholar