Skip to main content
Log in

A semantic study of the first-order predicate logic with uncertainty involved

  • Published:
Fuzzy Optimization and Decision Making Aims and scope Submit manuscript

Abstract

In this paper, we provide a semantic study of the first-order predicate logic for situations involving uncertainty. We introduce the concepts of uncertain predicate proposition, uncertain predicate formula, uncertain interpretation and degree of truth in the framework of uncertainty theory. Compared with classical predicate formula taking true value in \(\{0,1\}\), the degree of truth of uncertain predicate formula may take any value in the unit interval \([0,1]\). We also show that the uncertain first-order predicate logic is consistent with the classical first-order predicate logic on some laws of the degree of truth.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Adams, E. (1998). A primer of probability logic. Stanford: CSLI Pulications.

    MATH  Google Scholar 

  • Campos, C., Cozman, F., & Luna, J. (2009). Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence. Journal of Applied Logic, 7(2), 137–154.

    Article  MathSciNet  MATH  Google Scholar 

  • Chen, X., & Ralescu, D. A. (2011). A note on truth value in uncertain logic. Expert Systems with Applications, 38(12), 15582–15586.

    Article  Google Scholar 

  • Cignoli, R., Esteva, F., Godo, L., & Torreas, A. (2000). Basic fuzzy logic is the logic of continuous t-norms and their residua. Soft Computing, 4(2), 106–112.

    Article  Google Scholar 

  • Coletti, G., & Scozzafava, R. (2002). Probability logic in a coherent setting. London: Kluwer.

    Book  Google Scholar 

  • Dubois, D., & Prade, H. (1987). Necessity measure and resolution principle. IEEE Transactions on Man Cybenet, 17(3), 474–478.

    Article  MathSciNet  MATH  Google Scholar 

  • Esteva, F., & Godo, L. (2001). Monoidal t-norm based logic: towards logic for left-continuous t-norms. Fuzzy Sets and Systems, 124(3), 271–288.

    Article  MathSciNet  MATH  Google Scholar 

  • Gödel, K. (1932). Zum intuitionistischen Aussagenkalköl. Anz. Akad. Wiss. Wien, 69, 65–66.

    MATH  Google Scholar 

  • Hailperin, T. (1996). Sentential probability logic. London: Associated University Presses.

    MATH  Google Scholar 

  • Hajek, P. (1998). Metamathematics of fuzzy logic. London: Kluwer.

    Book  MATH  Google Scholar 

  • Li, X., & Liu, B. (2009a). Foundation of credibilistic logic. Fuzzy Optimization and Decision Making, 8(1), 91–102.

    Article  MathSciNet  MATH  Google Scholar 

  • Li, X., & Liu, B. (2009b). Hybrid logic and uncertain logic. Journal of Uncertain Systems, 3(2), 83–94.

    Google Scholar 

  • Liu, B. (2007). Uncertainty theory (2nd ed.). Berlin: Springer.

    MATH  Google Scholar 

  • Liu, B. (2009a). Some research problems in uncertainty theory. Journal of Uncertain Systems, 3(1), 3–10.

    Google Scholar 

  • Liu, B. (2009b). Uncertain entailment and modus ponens in the framework of uncertain logic. Journal of Uncertain System, 3(4), 243–251.

    Google Scholar 

  • Liu, B. (2010). Uncertainty theory: A branch of mathematics for modeling human uncertainty. Berlin: Springer.

    Book  Google Scholar 

  • Liu, B. (2013). Uncertainty theory, 4th Edn. http://orsc.edu.cn/liu.

  • Nilsson, N. (1986). Probability logic. Artificial Intelligence, 28, 71–78.

    Article  MathSciNet  MATH  Google Scholar 

  • Pei, D. (2003). On equivalent forms of fuzzy logic systems NM and IMTL. Fuzzy Sets and Systems, 138(1), 187–195.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, G. (1997). A formal deduction system of fuzzy propositional calculation. Science in China Series E-information Sciences, 42(10), 1041–1044.

    Google Scholar 

  • Wang, S., & Wang, M. (2006). Disjunctive elimination rule and its application in MTL. Fuzzy Sets and Systems, 157, 3169–3176.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

We would like to express our gratitude to both the editor and the anonymous reviewers for their valuable comments that significantly improved the quality of this paper. This work was supported by National Natural Science Foundation of China (Nos. 61273044, 71101007, 71371027), Program for New Century Excellent Talents in University under Grant No. NCET-13-0649, and the Fundamental Research Funds for the Central Universities (No. ZZ1316)

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiang Li.

Appendix: Classical logic

Appendix: Classical logic

The section recalls some basic concepts and results about classical propositional logic and first-order predicate logic.

Let \(X\) be a formula containing propositions \(p_{1}, p_{2},\ldots ,p_{n}\). Then there is a Boole function \(f: \{0,1\}^{n}\rightarrow \{0,1\}\) such that \(T(X)=1\) if and only if \(f(x_{1}, x_{2},\ldots ,x_{n})=1\) where \(x_i=T(p_i)\) for \(i=1,2,\ldots ,n\). For simplicity, we denote the Boole function of formula \(X\) as \(f_{X}\).

Definition (a)

A formula \(X\) is called a tautology, denoted by \( \models X\), if \( f_{X}(x_{1},x_{2},\ldots , x_{n})=1\) for all \((x_{1},x_{2},\ldots ,x_{n})\in \{0,1\}^{n}.\)

Definition (b)

A formula \(X\) is said to be contradiction, denoted by \(\models \lnot X\), if \( f_{X}(x_{1},x_{2},\ldots ,x_{n})=0\) for all \((x_{1},x_{2},\ldots ,x_{n})\in \{0,1\}^{n}.\)

Definition (c)

Formulae \(X\) and \(Y\) are called semantically equivalent, denoted by \(X \equiv Y\), if \(f_{X}(x_{1},x_{2},\) \(\cdots ,x_{n})=f_{Y}(x_{1},x_{2},\ldots ,x_{n})\) for all \((x_{1},x_{2},\ldots ,x_{n})\in \{0,1\}^{n}.\)

Definition (d)

Let \(X\) be a formula containing propositions \(p_{1}, p_{2},\ldots ,p_{n}\). It is said to be a disjunctive normal form if

$$\begin{aligned} X&= (Q_{11}\wedge Q_{12}\wedge ...\wedge Q_{1n})\vee (Q_{21}\wedge Q_{22}\wedge \cdots \wedge Q_{2n})\vee \cdots \vee \\&(Q_{m1}\wedge Q_{m2}\wedge \cdots \wedge Q_{mn}), \end{aligned}$$

where \(Q_{ij} \) is either \(p_{j}\) or \( \lnot p_{j}\) for \(i=1,2,\ldots ,m,j=1,2,\ldots ,n.\)

Theorem (a)

Let \(X\) be a formula containing propositions \(p_{1}, p_{2},\ldots ,p_{n}\). Then it is semantically equivalent to a disjunctive normal form as follows:

$$\begin{aligned} \bigvee _{f_{X}(x_{1},x_{2},\ldots ,x_{n})=1,(x_{1},x_{2},\ldots ,x_{n})\in \{0,1\}^{n}} Q_{x_1}\cap Q_{x_2}\cap \cdots \cap Q_{x_n}, \end{aligned}$$

where for each \((x_{1},x_{2},\ldots ,x_{n})\in \{0,1\}^{n}\) with \(f_{X}(x)=1\), \(Q_{x_i}=p_{i}\) if \(x_{i}=1\) and \(Q_{x_i}=\lnot p_{i}\) if \(x_{i}=0\).

The disjunctive normal form of \(X\) is denoted by \(G(X)\). For example, \(G(p_{1}\wedge p_{2}\rightarrow p_{3})=(\lnot p_{1}\wedge p_{2}\wedge \lnot p_{3})\vee ( p_{1}\wedge \lnot p_{2}\wedge \lnot p_{3})\vee (\lnot p_{1}\wedge \lnot p_{2}\wedge \lnot p_{3}) \vee (p_{1}\wedge p_{2}\wedge \lnot p_{3})\vee (\lnot p_{1}\wedge p_{2}\wedge \lnot p_{3})\vee (p_{1}\wedge \lnot p_{2}\wedge \lnot p_{3})\vee (\lnot p_{1}\wedge \lnot p_{2}\wedge \lnot p_{3})\).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, X., Li, X. A semantic study of the first-order predicate logic with uncertainty involved. Fuzzy Optim Decis Making 13, 357–367 (2014). https://doi.org/10.1007/s10700-014-9184-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10700-014-9184-2

Keywords

Navigation