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Spreadsheets based on interval constraint satisfaction

Published online by Cambridge University Press:  27 February 2009

Eero Hyvönen
Eero Hyvönen
Affiliation:
Technical Research Centre of Finland, Laboratory for Information Processing, Lehtisaarentie 2A, 00340 Helsinki, Finland.
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Abstract

Spreadsheets are difficult to use in applications, where only incomplete or inexact data (e.g., intervals) are available-a typical situation in various design and planning tasks. It can be argued that this is due to two fundamental shortcomings of the computational paradigm underlying spreadsheets. First, the distinction between input and output cells has to be fixed before computations. Second, cells may have only exact values. As a result, spread-sheets support the user only with primitive iterative problem solving schemes based on trial-and-error methods. A constraint-based computational paradigm for next generation interval spreadsheets is presented. The scheme makes it possible to exploit incomplete/inexact data (intervals), and it can support problem solving in a top-down fashion. Current spreadsheets constitute a special case of the more general interval constraint spreadsheets proposed.

Keywords

spreadsheetsinterval constraint satisfactiontop-down refinement
Type
Research Article
Information
AI EDAM , Volume 8 , Issue 1 , Winter 1994 , pp. 27 - 34
Copyright
Copyright © Cambridge University Press 1994

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References

REFERENCES

Aiba, A., Sakai, Y., Sato, H., Hawley, D., & Hasegawa, R. (1988). Constraint logic programming language CAL. Proc. Int. Conf. Fifth Generation Computer Systems, Tokyo, 1988, Vol. 1, pp. 263–276. Institute for New Generation Computer Technology, Tokyo, Japan.CrossRefGoogle Scholar
Alefeld, G., & Herzberger, J. (1983). Introduction to Interval Computations. Addison-Wesley, Reading, Massachusetts.Google Scholar
Colmerauer, A. (1987). Opening the Prolog III universe. Byte (August), 177–182.Google Scholar
Davis, E. (1987). Constraint propagation with interval labels. Art. Intell. 8, 99–118.Google Scholar
Dechter, R., & Pearl, J. (1989). Tree clustering for constraint networks. Art. Intell. 38, 353–356.CrossRefGoogle Scholar
Elias, A.L. (1986). Knowledge engineering of the aircraft design process. In Knowledge Based Problem Solving (Kowalik, J.S., Ed.), pp. 213–256. Prentice-Hall, Englewood Cliffs, New Jersey.Google Scholar
Gosling, J. (1983). Algebraic constraints. Dissertation. Carnegie-Mellon University, Pittsburgh, Pennsylvania.Google Scholar
Heintze, N., Jaffar, J., Lassez, C., Lassez, J.-L., McAloon, K., Michaylov, S., Stuckey, P., & Yap, R. (1987). Constraint logic programming: A reader: An edited collection of papers. Fourth IEEE Symposium on Logic Programming, San Francisco, Aug. 31-Sept. 4, 1987.Google Scholar
Hyvönen, E. (1989). Constraint reasoning based on interval arithmetic. Proc. 11th IJCAI, Detroit, 1989, pp. 1193–1198. Morgan Kaufmann Publishers, Los Altos, California.Google Scholar
Hyvönen, E. (1991a). Constraint reasoning with incomplete knowledge. The tolerance propagation approach. Dissertation. Technical Research Centre of Finland, VTT Publications, Espoo, Finland.Google Scholar
Hyvönen, E. (1991b). Global consistency in interval constraint satisfaction. Proc. 3rd Scandinavian Conf. Artificial Intelligence 1991, pp. 241–251. IOS Press, Amsterdam.Google Scholar
Hyvönen, E. (1991c). Application conditions for interval constraint propagation. Proc. 11th Int. Workshop Expert Systems and Their Applications, pp. 269–282. Avignon, France.Google Scholar
Hyvönen, E. (1991d). Interval constraint spreadsheets for financial planning. Proc. 1st Int. Conf. Artificial Intelligence Applications on Wall Street. IEEE Press, New York.Google Scholar
Hyvönen, E. (1992). Constraint reasoning based on interval arithmetic. The tolerance propagation approach. Art. Intell. 58, 71–112.CrossRefGoogle Scholar
Hyvönen, E., De Pascale, S., & Lehtola, A. (1993a) Interval constraint satisfaction tool INC++, Proc. of 5th International Conference on Tools with Artificial Intelligence, pp. 298–305. IEEE Press, New York.Google Scholar
Hyvönen, E., De Pascale, S., & Lehtola, A. (1993b) Interval constraint programming in C++. In Mayoh, B., Tyugu, E., Penham, J., Constraint Programing, NATO Advanced Science Institute, Series F, Springer-Verlag, Germany, (in press).Google Scholar
Konopasek, M., & Jayaraman, S. (1984). The TK!Solver Book. McGraw-Hill, Berkeley, California.Google Scholar
Leler, W. (1988). Constraint Programming Languages. Their Specification and Generation. Addison-Wesley, Reading, Massachusetts.Google Scholar
Mackworth, A.K. (1987). Constraint satisfaction. In Encyclopedia of Artificial Intelligence, Vol. 1 (Shapiro, S., Ed.), pp. 205–211. John Wiley & Sons, New York.Google Scholar
Mackworth, A.K., & Freuder, E. (1985). The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Art. Intell. 25, 65–74.CrossRefGoogle Scholar
Meseguer, P. (1989). Constraint satisfaction problems: An overview. Al Commun. 2(1), 3–17.Google Scholar
Moore, R.E. (1966). Interval Analysis. Prentice-Hall, Englewood Cliffs, New Jersey.Google Scholar
Moore, R.E. (1979). Methods and Applications of Interval Analysis (SIAM Studies in Applied Mathematics). SIAM, Philadelphia.CrossRefGoogle Scholar
Ratscheck, H., & Rokne, J. (1984). Computer Methods for the Range of Functions. Ellis Horwood, Chichister, England.Google Scholar
Ratscheck, H., & Rokne, J. (1988). New Computer Methods for Global Optimization. Ellis Horwood, Chichister, England.Google Scholar
Steele, G.L. (1980). The definition and implementation of a computer programming language based on constraints. Dissertation. Massachusetts Institute of Technology, Cambridge, Massachusetts.Google Scholar
Waltz, D. (1975). Understanding line drawings of scenes with shadows. In The Psychology of Computer Vision. (Winston, P., Ed.), pp. 19–91. McGraw-Hill, New York.Google Scholar