Abstract
Engineering problem solving requires both domain knowledge and an understanding of how to apply that knowledge. While much of the recent work in qualitative physics has focused on building reusable domain theories, there has been little attention paid to representing the control knowledge necessary for applying these models. This paper shows how qualitative representations and compositional modeling can be used to create control knowledge for solving engineering problems. This control knowledge includes modeling assumptions, plans and preferences. We describe an implemented system, called TPS (Thermodynamics Problem Solver) that illustrates the utility of these ideas in the domain of engineering thermodynamics. To date, TPS has solved over 30 problems, and its solutions are similar to those of experts. We argue that our control vocabulary can be extended to most engineering problem solving domains and employed in a variety of problem solving architectures.
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Bibliography
de Kleer, J. (1975). Qualitative and quantitative knowledge in classical mechanics (Technical Report 352.). Cambridge, MA.: MIT Al Lab.
Dejong, G. F. (1986). Explanation based Learning, Machine Learning: An Artificial Intelligence Approach, Vol. II. Los Altos, CA: Morgan Kaufman.
Falkenhainer, B., & Forbus, K. (1991). Compositional modeling: Finding the right model for the job. Artificial Intelligence, 51, 95–143.
Forbus, K., & de Kleer, J. (1993). Building Problem Solvers: MIT Press.
Forbus, K., & Whalley, P. B. (1994). Using qualitative physics to build articulate software for thermodynamics education. In Proceedings of the International Joint Conference on Artificial Intelligence.
Kuipers, B. J., & Shults, B. (1994). Reasoning in logic about continuous systems. In Proceedings of the 8th International Workshop on Qualitative Reasoning about Physical Systems, Nara, Japan.
Laird, J., Rosenbloom, P., & Newell, A. (1986). Universal Subgoaling and Chunking: Kluwer Academic Publishers.
Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Models of competence in solving physics problems. Cognitive Science, 4(4), 317–345.
McAllester, D. (1978). A three-valued truth maintenance system. Ph.D Thesis, Department of Electrical Engineering, MIT, Cambridge, MA.
Pisan, Y. (1995). A Visual Routines Based Model of Graph Understanding. In Proceeding of the Seventeenth Annual Conference of the Cognitive Science Society, Hillsdale, NJ.
Priest, A., & Lindsay, R. (1992). New light on novice-expert differences in physics problem solving. British journal of Psychology(83), 389–405.
Sgouros, N. M. (1993). Representing physical and design knowledge in innovative engineering design. Ph.D Thesis, Department of Computer Science, Northwestern University, Evanston, IL.
Skorstad, G., & Forbus, K. (1990). Qualitative and quantitative reasoning about thermodynamics. In Proceedings of the 10th Annual Conference of the Cognitive Science Society.
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© 1997 Springer-Verlag Berlin Heidelberg
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Pisan, Y. (1997). Controlling engineering problem solving. In: Sattar, A. (eds) Advanced Topics in Artificial Intelligence. AI 1997. Lecture Notes in Computer Science, vol 1342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63797-4_103
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DOI: https://doi.org/10.1007/3-540-63797-4_103
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