Skip to main content
Springer Nature Link
Log in

The place of fuzzy logic in AI

  • Conference paper
  • First Online:
Fuzzy Logic in Artificial Intelligence (FLAI 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1566))

Included in the following conference series:

  • 1023 Accesses

Abstract

Fuzzy logic is more than thirty years old and has a long-lasting misunderstanding with Artificial Intelligence (A.I.), although the formalization of some forms of commonsense reasoning has motivated the development of fuzzy logic. What fuzzy sets typically brings to AI is a mathematical framework for capturing gradedness in reasoning devices. Moveover gradedness can take various forms: similarity between propositions, levels of uncertainty, and degrees of preference. The paper provides a brief survey of the fuzzy set contribution to the modelling of various types of commonsense reasoning, and advocates the complementarity of fuzzy set methods, and more generally of soft computing techniques, with symbolic A.I.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Assilian S. (1974) Artificial Intelligence in the Control of Real Dynamic Systems. PhD Thesis, Queen Mary College, University of London.

    Google Scholar 

  • Benferhat S., Dubois D., Prade H. (1992) Representing default rules in possibilistic logic. Proc. of the 3rd Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR’92), Cambridge, MA, Oct. 26–29, 673–684.

    Google Scholar 

  • Benferhat S., Dubois D., Lang J., Prade H. (1994) Hypothetical reasoning in possibilistic logic: basic notions, applications and implementation issues. In: Between Mind and Computer, Fuzzy Science and Engineering, Vol. I (P.Z. Wang, K.F. Loe, eds.), World Scientific Publ., Singapore, 1–29.

    Google Scholar 

  • Bersini H., Bontempi G. (1997) Now comes the time to defuzzify neuro-fuzzy models. Fuzzy Sets and Systems, 90, 161–169. Benferhat S., Dubois D., Prade H. (1992) Representing default rules in possibilistic logic. Proc. of the 3rd Inter. Conf. on Principles of Knowledge Representation and Reasoning (KR’92), Cambridge, MA, Oct. 26–29, 673–684.

    Google Scholar 

  • Castro J.L., Trillas S., Cubilla S. (1994) On consequence in approximate reasoning. J. of Applied Non-Classical Logics, 4, 91–103.

    MATH  Google Scholar 

  • Cayrac D., Dubois D., Haziza M., Prade H. (1994) Possibility theory in “fault mode effect analyses”—A satellite fault diagnosis application—Proc. of the IEEE World Cong. on Computational Intelligence, Orlando, FL, June 26–July 2, 1176–1181.

    Google Scholar 

  • Chakraborty M. (1988) Use of fuzzy set in introducing graded consequence in multiple-valued logic. In: Fuzzy Logic in Knowledge Based Systems-Decision and Control (M.M. Gupta, T. Yamakawa, eds) North-Holland, 247–257.

    Google Scholar 

  • Dubois D., Esteva F., Garcia P., Godo L., Prade H. (1997) Similarity-based consequence relations. Int. J. of Approximate Reasoning

    Google Scholar 

  • Dubois D., Fargier H., Prade H. (1994) Propagation and satisfaction of flexible constraints. In: Fuzzy Sets, Neural Networks, and Soft Computing (R.R. Yager, L.A. Zadeh, eds.), Van Nostrand Reinhold, New York, 166–187.

    Google Scholar 

  • Dubois D., Grabisch M., Prade H. (1994) Gradual rules and the approximation of control laws. In: Theoretical Aspects of Fuzzy Control (H.T. Nguyen, M. Sugeno, R. Tong, R. Yager, eds.), Wiley, New York, 147–181.

    Google Scholar 

  • Dubois D., Lang J., Prade H. (1994) Possibilistic logic. In: Handbook of Logic in Artificial Intelligence and Logic Programming, Vol. 3 (D.M. Gabbay, C.J. Hogger, J.A. Robinson, D. Nute, eds.), Oxford University Press, 439–513.

    Google Scholar 

  • Dubois D., Prade H. (1991) Fuzzy sets in approximate reasoning—Part 1: Inference with possibility distributions. Fuzzy Sets and Systems, 40, Part 1: 143–202; Part 2 (with Lang J.) Logical approaches: Fuzzy Sets and Systems, 40, 203–244.

    Article  MATH  MathSciNet  Google Scholar 

  • Dubois D., Prade H. (1995a) Possibility theory as a basis for qualitative decision theory. Proc. of the 14th Inter. Joint Conf. on Artificial Intelligence (IJCAI’95), Montreal, Canada, Aug. 19–25, 1924–1930

    Google Scholar 

  • Dubois, D., Prade H. (1995b) What does fuzzy logic bring to AI? ACM Computing Surveys, 27, 328–330.

    Article  Google Scholar 

  • Dubois D., Prade H. (1996a) What are fuzzy rules and how to use them. Fuzzy Sets and Systems, 84, 169–185.

    Article  MATH  MathSciNet  Google Scholar 

  • Dubois D., Prade H. (1997) The three semantics of fuzzy sets. Fuzzy Sets and Systems, 90, 141–150.

    Article  MATH  MathSciNet  Google Scholar 

  • Dubois D., Prade H. (1996b) New trends and open problems in fuzzy logic and approximate reasoning. Theoria (San Sebastian, Spain), Vol. 11, no 27, 109–121.

    MATH  MathSciNet  Google Scholar 

  • Dubois D., Prade H. (1998) Soft computing, fuzzy logic, and artificial intelligence. Soft Computing, 2, to appear

    Google Scholar 

  • Elkan C. (1994) The paradoxical success of fuzzy logic. (with discussions by many scientists and a reply by the author) IEEE Expert, August, 3–46.

    Google Scholar 

  • Gärdenfors P. (1988) Knowledge in Flux—Modeling the Dynamics of Epistemic States. The MIT Press, Cambridge, MA.

    Google Scholar 

  • Hájek P. (1995) Fuzzy logic as logic. In: Mathematical Models for Handling Partial Knowledge in Artificial Intelligence (G. Coletti, D. Dubois, R. Scozzafava, eds.), Plenum Press, New York, 21–30.

    Google Scholar 

  • Holmblad L.P., Østergaard J.J. (1997) The progression of the first fuzzy logic control application. In: Fuzzy Information Engineering: A Guided Tour of Applications (D. Dubois, H. Prade, Yager R.R., eds.), Wiley, New York, 343–356.

    Google Scholar 

  • Klawonn F., Kruse R. (1993) Equality relations as a basis for fuzzy control. Fuzzy Sets and Systems, 54, 147–156.

    Article  MATH  MathSciNet  Google Scholar 

  • Kruse, R., Gebhardt J., Klawonn F. (1994) Foundations of Fuzzy Systems. John Wiley, Chichester, West Sussex, UK.

    MATH  Google Scholar 

  • Lehmann D., Magidor M. (1992) What does a conditional knowledge base entail? Artificial Intelligence 55, 1–60.

    Article  MATH  MathSciNet  Google Scholar 

  • Li D., Liu D.B. (1990) A fuzzy PROLOG database system. Wiley, New York.

    Google Scholar 

  • Lee R.C.T. (1972) Fuzzy logic and the resolution principle. J. Assoc. Comput. Mach. 19, 109–119

    MATH  MathSciNet  Google Scholar 

  • Mukaidono M. Shen Z.L. Ding L. (1989) Fundamentals of fuzzy Prolog. Int. J. Approx. Reasoning, 3, 179–193

    Article  MATH  Google Scholar 

  • Mamdani E.H., Assilian S. (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. of Man-Machine Studies, 7, 1–13.

    Article  MATH  Google Scholar 

  • Martin T.P., Ralescu A.L. (Eds.) Fuzzy Logic in Artificial Intelligence. Towards Intelligent Systems. (Proc. IJCAI’95 Workshop, Montreal, Canada)

    Google Scholar 

  • Mendel J. (1995) Fuzzy logic systems for engineering: A tutorial. Proc. IEEE, 83, 345–377.

    Article  Google Scholar 

  • Novak V. (1996) Paradigm, formal properties and limits of fuzzy logic. Int. J. of General Systems, 24, 377–406.

    MATH  Google Scholar 

  • Pearl J. (1990) System Z: a natural ordering of defaults with tractable applications to default reasoning. Proc. of the 3rd Conf. on the Theoretical Aspects of Reasonig About Knowledge (TARK’90), Morgan and Kaufmann, 121–135.

    Google Scholar 

  • Peng Y., Reggia (1990) Abductive Inference Models for Diagnostic Problem-Solving. Springer Verlag, New York.

    MATH  Google Scholar 

  • Ralescu A. (Ed.) (1994) Fuzzy Logic in Artificial Intelligence (Proc. IJCAI’93 Workshop, Chambery, France). Lecture Notes in Artificial Intelligence, Vol. 847, Springer Verlag, Berlin

    Google Scholar 

  • Ruspini E. (1991) On the semantics of fuzzy logic. Int. J. of Approximate Reasoning, 5, 45–88.

    Article  MATH  MathSciNet  Google Scholar 

  • Verbruggen H.B., Bruijn P.M. (1997) Fuzzy control and conventional control: What is (and can be) the real contribution of fuzzy systems? Fuzzy Sets and Systems, 90, 151–160.

    Article  MATH  MathSciNet  Google Scholar 

  • Sanchez E. (1977) Solutions in composite fuzzy relations equations—Application to medical diagnosis in Brouwerian logic. In: Fuzzy Automated and Decision Processes (M.M. Gupta et al., eds.) North-Holland, 221–234.

    Google Scholar 

  • Yager R.R. (1995) Fuzzy sets as a tool for modelling. In: Computer Science Today. Recent trends and Developments. Lecture Notes in Artificial Intelligence, Vol. 1000, Springer Verlag, Berlin, 536–548.

    Google Scholar 

  • Yager R.R. (1997) Fuzzy logics and artificial intelligence. Fuzzy Sets and Systems, 90, 193–191.

    Article  MathSciNet  Google Scholar 

  • Zadeh L.A. (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Systems, Man and Cybernetics, 3, 28–44.

    MATH  MathSciNet  Google Scholar 

  • Zadeh L.A. (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, 3–28.

    Article  MATH  MathSciNet  Google Scholar 

  • Zadeh L.A. (1979) A theory of approximate reasoning. In: Machine Intelligence, Vol. 9 (J.E. Hayes, D. Mitchie, L.I. Mikulich, eds.), Wiley, New York, 149–194.

    Google Scholar 

  • Zadeh L.A. (1996) Fuzzy logic=computing with words. IEEE Trans. on Fuzzy Systems, 4, 103–111.

    Article  Google Scholar 

  • Zadeh L.A. (1997a) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90, 111–127.

    Article  MATH  MathSciNet  Google Scholar 

  • Zadeh L.A. (1997b) What is soft computing? Soft Computing, 1(1), p. 1.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse (IRIT), Université Paul Sabatier, 118 route de Narbonne, 31062, Toulouse Cédex 4, France

    Didier Dubois & Henri Prade

Authors
  1. Didier Dubois
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Henri Prade
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Anca L. Ralescu James G. Shanahan

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dubois, D., Prade, H. (1999). The place of fuzzy logic in AI. In: Ralescu, A.L., Shanahan, J.G. (eds) Fuzzy Logic in Artificial Intelligence. FLAI 1997. Lecture Notes in Computer Science, vol 1566. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095068

Download citation

  • DOI: https://doi.org/10.1007/BFb0095068

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66374-4

  • Online ISBN: 978-3-540-48358-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation