Abstract
It has been recognised that formal methods are useful as a modelling tool in requirements engineering. Specification languages such as Z permit the precise and unambiguous modelling of system properties and behaviour. However some system problems, particularly those drawn from the IS problem domain, may be difficult to model in crisp or precise terms. It may also be desirable that formal modelling should commence as early as possible, even when our understanding of parts of the problem domain is only approximate. This paper suggests fuzzy set theory as a possible representation scheme for this imprecision or approximation. We provide a summary of a toolkit that defines the operators, measures and modifiers necessary for the manipulation of fuzzy sets and relations. We also provide some examples of the laws which establishes an isomorphism between the extended notation presented here and conventional Z when applied to boolean sets and relations.
The authors would like to thank Dr. Roger Duke (Dept of Computer Science and Electrical Engineering, University of Queensland, Aus.) and Mr. Steve Dunne (School of Computing and Mathematics, University of Teesside, U.K.) for their constructive comments and suggestions made during the preparation of this paper.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
G.M. Allinger, S.L. Feinzig, and E.A. Janak. Fuzzy Sets and Personnel Selection: Discussion and an Application. Journal of Occupational and Organizational Psychology, 66:162–169, 1993.
G. Bojadziev and M. Bojadziev. Fuzzy Sets, Fuzzy Logic, Applications. World Scientific, Singapore, 1995.
P. Checkland. Systems Thinking, Systems Practice. John Wiley and Sons, Chichester, 1981.
P. Checkland and Jim Scholes. Soft Systems Methodology in Action. John Wiley and Sons, Chichester, 1990.
E. Cox. The Fuzzy Systems Handbook. AP Professional-Harcourt Brace & Company, Boston, 1994.
D. Dubois and H. Padre. Fuzzy Sets and Systems: Theory and Applications. Academic Press, Inc, 1980.
D. Dubois and H. Prade. Possibility Theory-An Approach to Computerised Processing of Uncertainty. Plenum Press, New York, 1988.
R. Duke, P. King, G. Rose, and G. Smith. The Object-Z Specification Language: Version 1. Technical Report 91-1, Dept of Computer Science, University of Queensland, 1991.
R. Duke, G. Rose, and G. Smith. Object-Z: A Specification Language Advocated for the Description of Standards. Computer Standards and Interfaces, 17:511–533, 1995.
I. Graham. Fuzzy Logic in Commercial Expert Systems-Results and Prospects. Fuzzy Sets and Systems, 40:451–472, 1991.
B. Hesketh, R. Pryor, and M. Gleitzman. Fuzzy Logic: Toward Measuring Gottfredson’s Concept of Occupational Social Space. Journal of Counselling Psychology, 36(1):103–109, 1989.
J. Jacky. The Way of Z: Practical Programming with Formal Methods. Cambridge University Press, Cambridge, 1997.
Jyh-Shing Roger Jang, Chuen-Tsai Sun, and Eiji Mizutani. Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Inc., New Jersey, 1997.
J. Kacprzyk, M. Fedrizzi, and H. Nurmi. Fuzzy Logic with Linguistic Quantifiers in Group Decision Making. In R.R. Yager and L.A. Zadeh, editors, An Introduction to Fuzzy Logic Applications in Intelligent Systems, pages 263–280. Kluwer Academic, 1992.
A. Kaufmann. Introduction to the theory of Fuzzy Subsets, volume 1-Fundamental Theoretical Elements. Academic Press, London, 1975.
J. Klir and D. Harmanec. Types and Measures of Uncertainty. In Janusz Kacprzyk, Hannu Nurmi, and Mario Fedrizzi, editors, Consensus under Fuzziness, pages 29–51. Kluwer Academic, 1997.
B. Kosko. Fuzziness vs. Probability. Int. J. General Systems, 17:211–240, 1990.
B. Kosko. Neural Networks and Fuzzy Systems. Prentice-Hall, New Jersey, 1992.
R. Kruse, J. Gebhardt, and F. Klawonn. Foundations of Fuzzy Systems. John Wiley & Sons, Chichester, 1994.
J. Lee and J. Yen. Specifying Soft Requirements of Knowledge-Based Systems. In R.R Yager and L.A. Zadeh, editors, Fuzzy Sets, Neural Networks, and Soft Computing, pages 285–295. Van Nostrand Reinhold, New York, 1994.
R. Lowen. Fuzzy Set Theory: Basic Concepts, Techniques and Bibliography. Kluwer Academic, Dordrecht, 1996.
A. De Luca and S. Termini. A Definition of a Nonprobablistic Entropy in the Setting of Fuzzy Set Theory. Information and Control, 20:301–312, 1972.
C. Matthews and P. A. Swatman. Fuzzy Z? In The Second Australian Workshop on Requirements Engineering (AWRE’97), pages 99–114. Macquarie University, Sydney, 1997.
C. Matthews and P. A. Swatman. Fuzzy Concepts and Formal methods: Some Illustrative Examples. Technical Report1999:37, School of Management Information Systems, Deakin University, 1999.
C. Matthews and P. A. Swatman. Fuzzy Z-The Extended Notation (Version 0). Technical Report 1999:38, Rev 1, School of Management Information Systems, Deakin University, 1999.
S. E. Newstead. Quantifiers as Fuzzy Concepts. In T. Zetenyi, editor, Fuzzy Sets in Psychology, pages 51–72. Elsevier Science Publishers B.V, North-Holland, 1988.
J. Nichols (ed.). Z notation— version 1.3. Technical report, ISO, June 1998.
B. Potter, J. Sinclair, and D. Till. An Introduction to Formal Specification and Z. Prentice Hall International Series in Computer Science, Hemel Hempstead, second edition, 1996.
H. Saiedian. Formal Methods in Information Systems Engineering. In R.H Thayer and M. Dorfman, editors, Software Requirements Engineering, pages 336–349. IEEE Computer Society Press, second edition, 1997.
K.J. Schmucker. Fuzzy Sets, Natural Language Computations, and Risk Analysis. Computer Science Press, Rockville, 1984.
M. Smithson. Fuzzy Set Analysis for Behavioral and Social Sciences. Springer-Verlag, New York, 1987.
M. Smithson. Ignorance and Uncertainty-Emerging Paradigms. Springer-Verlag, New York, 1988.
J.M Spivey. The Z Notation: A Reference Manual. Prentice Hall International Series in Computer Science, Hemel Hempstead, second edition, 1992.
P.A. Swatman. Formal Object-Oriented Method-FOOM. In H. Kilov and W. Harvey, editors, Specification of Behavioural Semantics in Object-Oriented Information Systems, pages 297–310. Kluwer Academic, 1996.
P. A. Swatman and P. M. C. Swatman. Formal Specification: An Analytical Tool for (Management) Information Systems. Journal of Information Systems, 2(2):121–160, April 1992.
I. Toyn. Innovations in the Notation of Standard Z. In J.P Bowen, A. Fett, and M. G. Hinchey, editors, ZUM’ 98: The Z Formal Specification Notation, Lecture Notes in Computer Science. Springer-Verlag, 1998.
G. Viot. Fuzzy Logic: Concepts to Constructs. AI Expert, 8(11):26–33, November 1993.
M. Viswanathan, M. Bergen, S. Dutta, and T. Childers. Does a Single Response Category in a Scale Completely Capture a Response? Psychology and Marketing, 13(5):457–479, 1996.
P. Wang. The Interpretation of Fuzziness. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 26(2):312–326, Apr 1996.
B. Wilson. Systems: Concepts, Methodologies and Applications. John Wiley and Sons, Chichester, second edition, 1990.
J. M. Wing. A Specifier’s Introduction to Formal Methods. IEEE Computer, 23(9):8–24, 1990.
Y. Y. Yao. A comparative study of rough sets and fuzzy sets. Journal of Information Sciences, 109:227–242, 1998.
L. A. Zadeh. Fuzzy Sets. Information and Control, 8:338–353, 1965.
L. A. Zadeh. The Concept of a Linguistic Variable and its Application to Approximate Reasoning I. Informzation Sciences, 8(4):199–249, 1975.
L. A. Zadeh. Fuzzy Logic. IEEE Computer, 21(4):83–92, April 1988.
L. A. Zadeh. Knowledge Representation in Fuzzy Logic. In R.R. Yager and L.A. Zadeh, editors, An Introduction to Fuzzy Logic Applications in Intelligent Systems, pages 1–25. Kluwer Academic, 1992.
A.C. Zimmer. A Common Framework for Colloquil Quantifiers and Probability Terms. In T. Zetenyi, editor, Fuzzy Sets in Psychology, pages 73–89. Elsevier Science Publishers B.V, North-Holland, 1988.
R. Zwick, D. V. Budescu, and T. S. Wallsten. An empirical study of the integration of linguistic probabilities. In T. Zetenyi, editor, Fuzzy Sets in Psychology, pages 91–125. Elsevier Science Publishers B.V, North-Holland, 1988.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Matthews, C., Swatman, P.A. (2000). Fuzzy Concepts and Formal Methods: A Fuzzy Logic Toolkit for Z. In: ZB 2000: Formal Specification and Development in Z and B. ZB 2000. Lecture Notes in Computer Science, vol 1878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44525-0_29
Download citation
DOI: https://doi.org/10.1007/3-540-44525-0_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67944-8
Online ISBN: 978-3-540-44525-8
eBook Packages: Springer Book Archive