A new method for implementation of geometric theorems

Document Type : Original Article

Authors

1 Department of mathematics and applications, University of Mohaghegh Ardabili, Ardabil, Iran

2 University of CentraleSupélec, Metz 57070, France

Abstract

This paper outlines the optimization of the two first parts of the three major components of the  scene descriptions of the geometrical shapes, namely (1) fuzzy logic scheme, (2) an integrated algebraic and logical reasoning, and (3) the machine learning technique. After arguing the need for using fuzzy expressions in spatial reasoning, the integration of approximate references into spatial reasoning using absolute measurements is outlined. The integration here means that the satisfiability of a spatial expression including possibly fuzzy one is conducted by both logical and algebraic reasoning. Then, the implementation of spatial expression evaluation is briefly described. The paper ends by the conclusion and the problems to be studied. The reasoning algorithm of this system not only uses the one-piece system, algebraic method and first-order logical method, but by introducing fuzzy spatial propositions and how to integrate them, as well as classical propositions in this system, it has a high power compared to classical systems.

Keywords

Main Subjects


Kutzler, B. (1988). Algebraic approaches to automated geometry theorem proving. Ph.D. thesis, University of Linz, Austeria.

Matsuyama, T. ,& Nitta, T. (1995). Geometric theorem proving by integrated logical and algebraic reasoning. Artificial intelligence, 75(1), 93-113.  

Kapur, D., & Mundy, J. L. (1988). Geometric reasoning and artificial intelligence: Introduction to the special volume. Artificial intelligence, 37(1-3), 1-11.
Fatholahzadeh, A., & Latifi., D. (2018) Knowledge representation for the geometrical shapes. Amirkabir university of technology, tehran, ICCG 1st Iranian conference on computational geometry, also in (2018). Journal of mathematics and system science 8(3), 77-83. DOI: 10.17265/2159-5291/2018.03.003.
Mohr, R., & Boufama, B.,& Brand, P. (1995) Understanding positioning from multiple images. Artificial Intelligence, 78(1-2), 213-238.

A. (2002) Fuzzy modeling of spatial expressions. The IASTED international conference on artificial intelligence and applications (AIA), M.H. Hamza (Ed.), ACTA Press, ISBN 0-88986- 301-6, Anaheim, CA, USA, 277-281.

Fatholahzadeh, A. (1996) reasoning with exact and approximate references in scene description. In: ASME, book VI, energy information management, Vol. I, computer in engineering, George Brown convention center, Houston, Texas 29 - Feb. 2, 80–88.

Fatholahzadeh, A., (1994). .Conceptualization of sample scene description. Turkish symposium on artificial intelligence and neural networks, C. Bozsahin, U. Halici, K. Oflazer and N. Yalabik (Eds.), Middle East Technical University and Bilkent University, ISBN 975-7679-07-0, Ankara, Turkey (pp. 91-100).

Brahman, R. J., & H. J.(1985) Reading in knowledge representation. Morgan Kaufamn Pub.

Fatholahzadeh, A. (1993, 2006). Traitement et Représentation des Connaissances: Méthodes, Algorithmes et Pro- Volumes, I and II, University of CentraleSupélec, France.

Corcoran, J. (1995) Universe of Cambridge dictionary of philosophy, Cambridge University Press, p. 941.

Eshragh, F., & Mamdani, E. H. (1981). A general approach to linguistic in: Mamdani et al. (Eds.), Academic Press, computer and people series (pp. 169–187).

Fatholahzadeh, A. (2003). Wrapping data for querying. The Journal of Scientia Iranica, Sharif University, Iran, 10(4), 454-463.

Kernighan, B., & Richie, D. (1988). C Programming Pearson; 2 edition.