Abstract
This paper describes a hybrid system for quantitative discovery and its related qualitative reasoning. The system HOTEP, is currently under development at MIT. The application area is the Failure Mechanisms in Pavement. The separate areas of Quantitative Discovery, Qualitative Reasoning, and Explanation-based generalization have been investigated by other researchers, but no attempt has been made to hybridize all of them together into a robust discovery system. Faced by a real need for knowledge in the evolving domain of the Failure mechanisms in pavement, we found an incentive to pursue the research in this direction. The paper emphasizes the importance of integrating mathematical induction tools with symbolic techniques to develop better understanding of the new field. Beside the discovery activities, the system is capable of handling competing theories. The quantitative discovery system in HOTEP has been implemented already, and it was tested in several areas. We propose harvesting knowledge bases from the sites so we can combine experience, then broadcast said synthesis back to the sites.
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References
Brachman, R.J., Fikes, R.E.& Levesque, H.J. KRYPTON: A Functional Approach to Knowledge Representation, pages 67–73. IEEE, October, 1983.
Doyle, Richard. Hypothesizing and Refining Causal Models. Technical Report AIM 811, Massachusetts Institute Technology, December 1984.
El-Shafei, Nayel S. Utilzing Quantitative Discovery in Qualitative Reasoning about Failure Mechanisms in Pavement. Master’s thesis, Massachusetts Institute Technology, 1985.
Falkenhainer, Brian. ABACUS —Adding Domain Constraints to quantitative Scientific Discovery. Technical Report UIUC-DCS-F-84–1195 (1SG 84–7), University of Illinois, Urbana-Champaign, 1984.
Findakly, Hani K. Stochastic Approach to the analysis of highway pavements. Master’s thesis, Massachusetts Institute Of Technology, 1971.
Forbus, Kenneth. Qualitative Process Theory. Technical Report AI-TR-789, Massachusetts Institute Technology, AI Lab, July 1984.
Golabi, Kamal, Kulkarni, N. Statistical State Transition in Pavement Management Systems. ASCE Interfaces, 1983, 1983, 30–55.
Holland, John. Escaping Brittleness: The possibility of General Purpose Learning Algorithms applied to parallel rule-based systems. Technical Report 111, University of Michigan, 1984. A discussion of the problem of Credit Assignment.
Langley, P. & Simon, H. The Search for Regularity: Four Aspects of Scientific Discovery. Technical Report CMU-RI-TR-84–20, Carnegie-Mellon University, 1984.
Markow, M. & Brademeyer, B. EAROMAR-II, Technical Report. Technical Report FHWA/RD-82/086, FHWA, 1984.
Michalski,Ryszard, A Theory and Methodology for Inductive Learning. In Michalski, R. et al. (Ed.), Machine Learning, Palo Alto,CA: Tioga Pub., 1984.
Minsky,Marvin, A Framework for representing knowledge. In Winston,Patrick (Ed.), The Psychology of Computer Vision, New York: McGraw Hill Book Co., 1975.
Minsky, Marvin. Society of Mind Cambridge,MA: Simon and Schuster, 1986.
Mitchell, T., Keller, R., & Kedar-Cabelli, S. Explanation-Based Generalization, A Unifying View. Technical Report ML-TR-2, Lab for C.S.Research/The State Univ.of N.J.,Rutgers, August 1985.
Moavenzadeh, F. & Brademeyer, B. A Stochastic Model for Pavement Performance and Management. Fourth International Conference on the structural design of Asphalt Pavements, Ann Arbor, MI, 1977.
Mooney,Raymond & DeJong, Gerald. I earning Schemata for Natural Language Processing, pages 681–687. IJCAI, Los Angeles,CA, 1985.
Pople, H.Jr. Heuristic methods for imposing structure on ill-structured problems:The structuring of Medical Diagnosis. In P. Szolovits (Ed.), Artificial Intelligence in Medicine. AAAS, 1979. The idea of Synthesized Links of Generalization as a vehicle for Polymorphism is proposed as a remedy.
Rich, Charles. The Layered Architecture of a System for Reasoning about Programs. IJCAI, Los Angclcs,CA, 1985.
Smillie, K.W. An Introduction to Regression and Correlation. London, U.K.: Academic Press, 1966.
Taylor, E.S. Dimensional Analysis for Engineers. Oxford, U.K.:Clarendon Press, 1974.
Williams,Brian C. Qualitative Analysis of MOS Circuits. Technical Report AI-TR-767, Massachusetts Institute Technology, AI Lab, July 1984.
Yang, Nai. Design of Functional Pavements. New York:McGraw Hill, 1972.
Yoder, E. & Witczak M. Principles of Pavement Design. New York:John Wiley Inc..,, 1975.
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© 1986 Springer-Verlag Berlin Heidelberg
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El-Shafei, N. (1986). Quantitative Discovery and Reasoning about Failure Mechanisms in Pavement. In: Sriram, D., Adey, R. (eds) Applications of Artificial Intelligence in Engineering Problems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-21626-2_48
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DOI: https://doi.org/10.1007/978-3-662-21626-2_48
Publisher Name: Springer, Berlin, Heidelberg
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