Formal fuzzy logic: Difference between revisions

From Citizendium
Jump to navigation Jump to search
imported>Giangiacomo Gerla
imported>Giangiacomo Gerla
Line 42: Line 42:
* Klir G. and Folger T., ''Fuzzy Sets, Uncertainty, and Information'' (1988), ISBN 0-13-345984-5.
* Klir G. and Folger T., ''Fuzzy Sets, Uncertainty, and Information'' (1988), ISBN 0-13-345984-5.
* Klir G. and Bo Yuan, ''Fuzzy Sets and Fuzzy Logic'' (1995) ISBN 0-13-101171-5
* Klir G. and Bo Yuan, ''Fuzzy Sets and Fuzzy Logic'' (1995) ISBN 0-13-101171-5
* [[Bart Kosko]], ''Fuzzy Thinking: The New Science of Fuzzy Logic'' (1993), Hyperion. ISBN 0-7868-8021-X
* Gerla G., Effectiveness and Multivalued Logics, ''Journal of Symbolic Logic'', 71 (2006) 137-162.
* Gerla G., Effectiveness and Multivalued Logics, ''Journal of Symbolic Logic'', 71 (2006) 137-162.
* Montagna F., Three complexity problems in quantified fuzzy logic. ''Studia Logica'', 68,(2001), 143-152.
* Montagna F., Three complexity problems in quantified fuzzy logic. ''Studia Logica'', 68,(2001), 143-152.

Revision as of 03:25, 28 June 2007

Formal fuzzy logic: a new chapter of multi-valued logic

Given a first order language, in fuzzy logic an interpretation is obtained by a domain D and by associating every constant with an element of D every n-ary operation name with an n-ary function in D and every n-ary predicate name by an n-ary fuzzy relation in D, i.e. a map r from to [0,1]. In accordance with the fact that vagues properties and relations are admitted, these fuzzy relation are not necessarily crisp. Such a kind of semantics was proposed long time by people interested in multi-valued logic, obviously. As an example, since 1996 in the book "Continuous model theory" by Chang and Kisler all the main semantics notions of fuzzy logic where defined in a theoretical setting. Now, even if from a semantical point of view fuzzy logic is not different from first order multi-valued logic, in the deduction apparatus one manifests a basic difference. In fact, in multi-valued logic the deduction operator is a tool to associate every (classical) set of axioms with the related (classical) set of theorems. Also, several authors limite our attention to the generation of the set of valid formulas. From such a point of view the paradigm of the deduction in multi-valued logic is not different in nature from the one of classical logic. Instead in fuzzy logic the notion of approximate reasoning is crucial. This notion is based on the one of fuzzy set of logical axioms and graded inference rules and it enables us to associate any fuzzy set of proper axioms with the related fuzzy subset of consequences. Onother basic difference is in the origin and in the agenda. Indeed the origin of fuzzy logic is in control and its aim is to find applications, in general. Instead, multi-valued logic originates from philosophical and theoretical questions.


Effectiveness for fuzzy logic

The notions of a "decidable subset" and "recursively enumerable subset" are basic ones for classical mathematics and classical logic. Then, the question of a suitable extension of such concepts to fuzzy set theory arises. A first proposal in such a direction was made by E.S. Santos by the notions of fuzzy Turing machine, Markov normal fuzzy algorithm and fuzzy program. Successively, L. Biacino and G. Gerla proposed the following definition where Ü denotes the set of rational numbers in [0,1].

Definition A fuzzy subset μ : S [0,1] of a set S is recursively enumerable if a recursive map h : S×N Ü exists such that, for every x in S, the function h(x,n) is increasing with respect to n and μ(x) = lim h(x,n). We say that μ is decidable if both μ and its complement –μ are recursively enumerable.

An extension of such a theory to the general case of the L-subsets is proposed in a paper by G. Gerla. In such a paper one refer to the theory of effective domains. It is an open question to give supports for a Church thesis for fuzzy logic claiming that the proposed notion of recursive enumerability for fuzzy subsets is the adequate one. To this aim, further investigations on the notions of fuzzy grammar and fuzzy Turing machine should be necessary (see for example Wiedermann's paper).


Define the set Val of valid formulas as the set of formulas assuming constantly value equal to 1. Then it is possible to prove that among the usual first order logics only Goedel logic has a recursively enumerable set of valid formulas. In the case of Lukasiewicz and product logic, for example, Val is not recursively enumerable (see B. Scarpellini, and Belluce, also such a fact was extensively examined in the book of Hajek). Neverthless, from these results we cannot conclude that these logics are not effective and therefore that an axiomatization is not possible. Indeed, if we refer to the just exposed notion of effectiveness for fuzzy sets, then the following theorem holds true (provided that the deduction apparatus of the fuzzy logic satisfies some obvious effectiveness property).

Theorem. Any axiomatizable fuzzy theory is recursively enumerable. In particular, the fuzzy set of logically true formulas is recursively enumerable in spite of the fact that the crisp set of valid formulas is not recursively enumerable, in general. Moreover, any axiomatizable and complete theory is decidable.

It is an open question to utilize the notion of recursively enumerable fuzzy subset to find an extension of[Gödel]]’s theorems to fuzzy logic.

See also

Bibliography

  • Biacino L., Gerla G., Fuzzy logic, continuity and effectiveness, Archive for Mathematical Logic, 41, (2002), 643-667.
  • Chang C. C.,Keisler H. J., Continuous Model Theory, Princeton University Press, Princeton, 1996.
  • Cignoli R., D’Ottaviano I. M. L. , Mundici D. , ‘’Algebraic Foundations of Many-Valued Reasoning’’. Kluwer, Dordrecht, 1999.
  • Cox E., The Fuzzy Systems Handbook (1994), ISBN 0-12-194270-8
  • Elkan C.. The Paradoxical Success of Fuzzy Logic. November 1993. Available from Elkan's home page.
  • Hájek P., Metamathematics of fuzzy logic. Kluwer 1998.
  • Hájek P., Fuzzy logic and arithmetical hierarchy, Fuzzy Sets and Systems, 3, (1995), 359-363.
  • Klir G. and Folger T., Fuzzy Sets, Uncertainty, and Information (1988), ISBN 0-13-345984-5.
  • Klir G. and Bo Yuan, Fuzzy Sets and Fuzzy Logic (1995) ISBN 0-13-101171-5
  • Gerla G., Effectiveness and Multivalued Logics, Journal of Symbolic Logic, 71 (2006) 137-162.
  • Montagna F., Three complexity problems in quantified fuzzy logic. Studia Logica, 68,(2001), 143-152.
  • Novák V., Perfilieva I, Mockor J., Mathematical Principles of Fuzzy Logic, Kluwer Academic Publishers, Dordrecht, (1999).
  • Scarpellini B., Die Nichaxiomatisierbarkeit des unendlichwertigen Prädikatenkalküls von Łukasiewicz, J. of Symbolic Logic, 27,(1962), 159-170.
  • Zimmermann H., Fuzzy Set Theory and its Applications (2001), ISBN 0-7923-7435-5.
  • Kevin M. Passino and Stephen Yurkovich, Fuzzy Control, Addison Wesley Longman, Menlo Park, CA, 1998.
  • Wiedermann J. , Characterizing the super-Turing computing power and efficiency of classical fuzzy Turing machines, Theor. Comput. Sci. 317, (2004), 61-69.
  • Zadeh L.A., Fuzzy algorithms, Information and Control, 5,(1968), 94-102.
  • Zadeh L.A., Fuzzy Sets, ‘’Information and Control’’, 8 (1965) 338­353.
  • Zemankova-Leech, M., Fuzzy Relational Data Bases (1983), Ph. D. Dissertation, Florida State University.