Hostname: page-component-89b8bd64d-72crv Total loading time: 0 Render date: 2026-05-07T16:36:50.972Z Has data issue: false hasContentIssue false
  • English
  • Français

Deriving a construct from site specific data: a knowledge level analysis

Published online by Cambridge University Press:  27 February 2009

Anastasios Dimitropoulos
Anastasios Dimitropoulos
Affiliation:
Department of Informatics, T.Y.P.A. Building, Panepistimioupolis, University of Athens, Zografou 157 71, Athens, Greece
Get access Rights & Permissions [Opens in a new window]

Abstract

In wood engineering design, an important task is the derivation of a construct from site specific data. Human experts perform the task in two phases, first qualitatively and then quantitatively in a hierarchical fashion. COWEN (Computer Wood ENgineer) is a fully implemented research prototype expert system that performs the qualitative phase and makes two contributions to the technology of expert systems. The first contribution is a Knowledge Level specification of the task prior to considering Symbol Level implementation. This is important because expert systems have been defined as mostly symbolic processors in the literature. The second contribution is that this Knowledge Level specification has led to the conclusion that additional qualitative sciences, besides physics and geometry, are needed for an engineering task. This is an interesting discovery because qualitative reasoning research in Artificial Intelligence (AI) has approached engineering design from the viewpoints of physics and geometry only.

Information

Type
Research Article
Information
AI EDAM , Volume 6 , Issue 3 , August 1992 , pp. 163 - 176
Copyright
Copyright © Cambridge University Press 1992

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.)

Article purchase

Temporarily unavailable

References

Addanki, S., Cremonini, R. and Penberthy, J. 1991. Graphs of models. Artificial Intelligence, 51, 145–177.CrossRefGoogle Scholar
Alexander, J. H., Freiling, M. J., Shulman, S. J., Staley, J. L., Rehfuss, S. and Messick, S. L. 1986. Knowledge level engineering: ontological analysis. Proceedings of National Conference on Artificial Intelligence, pp. 963–968.Google Scholar
Barr, A. and Feigenbaum, E. 1981. The Handbook of AI, Vol. 1. Los Altos, CA: W. Kaufmann.Google Scholar
Brachman, R. and Levesque, H. 1986. Tales from the far side of KRYPTON. Proceedings of the First International Conference on Expert Database Systems, pp. 3–43.Google Scholar
Breyer, D. E. 1980. Design of Wood Structures. New York: McGraw Hill.Google Scholar
Chandrasekaran, B. 1987. Towards a functional architecture for intelligence based on generic information processing tasks. Proceedings of International Joint Conference on Artificial Intelligence, pp. 1183–1192.Google Scholar
Chandrasekaran, B. 1989. Generic tasks as building blocks for knowledge based systems: the diagnosis and routine design examples, The Knowledge Engineering Review, 4, 183–210.Google Scholar
Clancey, W. 1992. Model construction operators, Artificial Intelligence, 53, 1–115.CrossRefGoogle Scholar
De Kleer, J. 1984. How circuits work. Artificial Intelligence, 24, 205–280.CrossRefGoogle Scholar
De Kleer, J. and Brown, J. 1981. Mental models of physical mechanisms and their acquisition. In: Cognitive Skills and their Acquisition ed. Anderson, J. R., Hillsdale, NJ: Erlbaum, 285–309.Google Scholar
De Kleer, J. and Brown, J. 1984. Qualitative physics based on confluences. Artificial Intelligence, 24, 7–83.CrossRefGoogle Scholar
De Mori, R. and Prager, R. 1989. Perturbation analysis with qualitative models. Proceedings of International Joint Conference on Artificial Intelligence, pp. 1180–1186.Google Scholar
Dormoy, J. L. and Raiman, O. 1988. Assembling a device. Proceedings of National Conference on Artificial Intelligence, pp. 330–335.Google Scholar
Doyle, R. 1989. Reasoning about hidden mechanisms. Proceedings of International Joint Conference on Artificial Intelligence, pp. 1343–1349.Google Scholar
Freas, A. D., Moody, R. C. and Soltis, L. A. (Eds) 1986. Wood: engineering design concepts, Vol. IV. University Park, PA: C. C. Heritage Memorial Series on Wood.Google Scholar
Horowitz, E. and Sahni, S. 1984. Fundamentals of Data Structures. Rockville, MD: Computer Science Press.Google Scholar
Johnson, P. E., Zualkernan, I. and Garber, S. 1987. Specification of expertise. International Journal of Man-Machine Studies, 26, 161–181.CrossRefGoogle Scholar
Joskowicz, L. 1989. Simplification and abstraction of kinematic behaviors. Proceedings of International Joint Conference on Artificial Intelligence, pp. 1337–1342.Google Scholar
Joskowicz, L. and Addanki, S. 1988. From kinematics to shape: an approach to innovative design. Proceedings of National Conference on Artificial Intelligence, pp. 347–352.Google Scholar
Joskowicz, L. and Sacks, E. (1991). Computational kinematics. Artificial Intelligence, 51, 381–341.CrossRefGoogle Scholar
Keuneke, A. and Allemang, D. 1989. Exploring the No function in structure principle. Journal of Experimental and Theoretical Artificial Intelligence, 1, 79–89.CrossRefGoogle Scholar
Mittal, S. and Frayman, F. 1989. Towards a generic model of configuration tasks. Proceedings of International Joint Conference on Artificial Intelligence, pp. 1395–1401.Google Scholar
Murthy, S. and Addanki, S. 1987. PROMPT: An innovative design tool. Proceedings of National Conference on Artificial Intelligence, pp. 637–642.Google Scholar
Nayak, P., Joskowicz, L. and Addanki, S. 1991. Automated model selection using context dependent behaviors. Fifth International Workshop on Qualitative Physics, Austin, TX.Google Scholar
Newell, A. 1982. The knowledge level. Artificial Intelligence, 18, 87–127.CrossRefGoogle Scholar
Smithers, T., Conkie, A., Doheny, J., Logan, K., Millington, K. and Tang, Xi M. 1990. Design as intelligent behavior: an AI in design research programme. International Journal for Artificial Intelligence in Engineering, 5(2), 78–109.Google Scholar
Tong, C. 1987. Toward an engineering science of knowledge-based design. International Journal for Artificial Intelligence in Engineering, 2(3), 133–166.Google Scholar
Ulrich, K. and Seering, W. 1988. Function sharing in mechanical design. Proceedings of National Conference on Artificial Intelligence, pp. 342–346.Google Scholar
Yip, K. 1991. Understanding complex dynamics by visual and symbolic reasoning. Artificial Intelligence, 51, 179–221.CrossRefGoogle Scholar