- •Contents
- •Preface
- •1 Introduction
- •1.1 Issues of Vagueness
- •1.3 The Problem of the Fringe
- •1.4 Preview of the Rest of the Book
- •1.5 History and Scope of Fuzzy Logic
- •1.6 Tall People
- •1.7 Exercises
- •2 Review of Classical Propositional Logic
- •2.1 The Language of Classical Propositional Logic
- •2.2 Semantics of Classical Propositional Logic
- •2.3 Normal Forms
- •2.4 An Axiomatic Derivation System for Classical Propositional Logic
- •2.5 Functional Completeness
- •2.6 Decidability
- •2.7 Exercises
- •3.2 Semantics of Classical First-Order Logic
- •3.3 An Axiomatic Derivation System for Classical First-Order Logic
- •3.4 Exercises
- •4.1 Numeric Truth-Values for Classical Logic
- •4.2 Boolean Algebras and Classical Logic
- •4.3 More Results about Boolean Algebras
- •4.4 Exercises
- •5.2 Lukasiewicz’s Three-Valued Logic
- •5.3 Bochvar’s Three-Valued Logics
- •5.5 Normal Forms
- •5.7 Lukasiewicz’s System Expanded
- •5.8 Exercises
- •6.3 Exercises
- •7.3 Tautologies, Validity, and “Quasi-”Semantic Concepts
- •7.4 Exercises
- •8.3 Exercises
- •9.3 MV-Algebras
- •9.4 Exercises
- •11 Fuzzy Propositional Logics: Semantics
- •11.1 Fuzzy Sets and Degrees of Truth
- •11.2 Lukasiewicz Fuzzy Propositional Logic
- •11.3 Tautologies, Contradictions, and Entailment in Fuzzy Logic
- •11.4 N-Tautologies, Degree-Entailment, and N-Degree-Entailment
- •11.5 Fuzzy Consequence
- •11.7 T-Norms, T-Conorms, and Implication in Fuzzy Logic
- •11.9 Product Fuzzy Propositional Logic
- •11.10 Fuzzy External Assertion and Negation
- •11.11 Exercises
- •12 Fuzzy Algebras
- •12.2 Residuated Lattices and BL-Algebras
- •12.3 Zero and Unit Projections in Algebraic Structures
- •12.4 Exercises
- •13 Derivation Systems for Fuzzy Propositional Logic
- •13.1 An Axiomatic System for Tautologies and Validity in Fuzzy
- •13.2 A Pavelka-Style Derivation System for Fuzzy
- •13.3 An Alternative Axiomatic System for Tautologies and Validity in FuzzyL Based on BL-Algebras
- •13.7 External Assertion Axioms
- •13.8 Exercises
- •14.1 Fuzzy Interpretations
- •14.2 Lukasiewicz Fuzzy First-Order Logic
- •14.3 Tautologies and Other Semantic Concepts
- •14.4 Lukasiewicz Fuzzy Logic and the Problems of Vagueness
- •14.6 Product Fuzzy First-Order Logic
- •14.8 Exercises
- •15.3 An Axiomatic Derivation System for Fuzzy
- •15.4 Combining Fuzzy First-Order Logical Systems; External Assertion
- •15.5 Exercises
- •16 Extensions of Fuzziness
- •16.2 Fuzzy “Linguistic” Truth-Values
- •16.3 Other Fuzzy Extensions of Fuzzy Logic
- •16.4 Exercises
- •17 Fuzzy Membership Functions
- •17.2 Empirical Construction of Membership Functions
- •17.3 Logical Relevance?
- •17.4 Exercises
- •Bibliography
- •Index
10 |
Introduction |
research.11 Ruspini, Bonissone, and Predrycz (1998) is a good introduction to fuzzy logic in the broad sense.
Finally, we note that certain technologies advertise the use of “fuzzy logic.” Fuzzy logic rice cookers have been around for a decade or so, cookers that “[do] what a real cook does, using [their] senses and intuition when [they are] cooking rice, watching and intervening when necessary to turn heat up or down, and reacting to the kind of rice in the pot, the volume and the time needed” (Wu 2003, p. E1). And there are fuzzy logic washing machines, fuzzy logic blood pressure monitors, fuzzy logic automatic transmission systems in automobiles, and so forth. The “fuzzy logic” in these cases is the circuit logic built into microchips designed to handle fuzzy measurements. For more on fuzzy technologies see Hirota (1993).
1.6 Tall People
Visit the Web site http://members.shaw.ca/harbord/heights.html. This is fun and will get you thinking about what tall means.
1.7 Exercises
SECTION 1.2
1In his article “Vagueness,” Max Black claimed that all terms whose application involves use of the senses are vague. For example, we use color words like green and shape words like round to describe what we see—and both of these terms are vague. The sea sometimes appears greenish, and this is typically a borderline case of green—not really green, but not really not green. While the moon is round when full and not round when in one of its quarters, phases close to full are borderline cases of round for the moon—it’s not really round, but also not clearly not round.
Give examples of vague terms whose application involves each of the other senses: one for hearing, one for smell, one for taste, and one for touch. Show that your terms are vague by describing one or more borderline cases—cases of things to which the term does not clearly apply or clearly fail to apply.
2Show that each of the following terms is vague by giving an example of a borderline case: young, fun, husband, sport, stale, chair, many, flat, book, sleepy.
3Are any of the terms in question 2 also ambiguous? General? Relative? Give examples to support your claims.
11Not only would such a term make clear the distinction between formal fuzzy logic originating from Goguen’s work and Zadeh’s version of fuzzy logic; its use would also make it clear when attacks on “fuzzy logic” by logicians (such as Susan Haack [1979]) are targeting the claim that fuzzy logic “in the broad sense” is logic, rather than work done in formal fuzzy logic.
1.7 Exercises |
11 |
SECTION 1.3
4Produce a version of the Sorites paradox using the term rich.
5Can Sorites arguments always be constructed for terms that exhibit multidimensional vagueness (defined in footnote 6), or do they arise mainly in the case of unidimensional vagueness? Defend your position.