Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

A general framework for computing maximal contractions

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

This paper investigates the problem of computing all maximal contractions of a given formula set Γ with respect to a consistent set Δ of atomic formulas and negations of atomic formulas. We first give a constructive definition of minimal inconsistent subsets and propose an algorithmic framework for computing all minimal inconsistent subsets of any given formula set. Then we present an algorithm to compute all maximal contractions fromminimal inconsistent subsets. Based on the algorithmic framework and the algorithm, we propose a general framework for computing all maximal contractions. The computability of the minimal inconsistent subset and maximal contraction problems are discussed. Finally, we demonstrate the ability of this framework by applying it to the first-order language without variables and design an algorithmfor the computation of all maximal contractions.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

  1. Alchourrón C, Gärdenfors P, Makinson D. On the logic of theory change: partial meet contraction and revision functions. Journal of Symbolic Logic, 1985, 50(2): 510–530

    Article  MathSciNet  MATH  Google Scholar 

  2. Li W. A logical framework for evolution of specifications. In: Proceedings of the 5th European Symposium on Programming: Programming Languages and Systems, ESOP’ 94. 1994, 394–408

    Chapter  Google Scholar 

  3. Li W. A computational framework for convergent agents. In:Leung K, Chan L W, Meng H, eds. Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. Berlin / Heidelberg: Springer, 2000, 113–126

    Google Scholar 

  4. Li W. A development calculus for specifications. Science in China Series F: Information Sciences, 2003, 46(5): 390–400

    Article  MathSciNet  MATH  Google Scholar 

  5. Li W. R-calculus: an inference system for belief revision. The Computer Journal, 2007, 50(4): 378–390

    Article  Google Scholar 

  6. Li W. Logical verification of scientific discovery. Science in China Series F: Information Sciences, 2010, 53(4): 677–684

    Article  Google Scholar 

  7. Li W. Mathematical Logic: Foundations for Information Science, 1st ed. Basel: Bikhäuser, 2010

    Book  MATH  Google Scholar 

  8. Luo J, Li W. R-calculus without the cut rule. Science in China Series F: Information Sciences, 2011, 54(12): 2530–2543

    Article  MathSciNet  Google Scholar 

  9. Li H, Li L. Computing R-contraction for propositional logic is hard. In: Proceedings of the 2nd International Workshop on Education Technology and Computer Science. 2010, 260–263

    Google Scholar 

  10. Jiang D, Lou Y, Jin Y. A revision system based on delegate model for propositional logic. In: Proceedings of the 2nd International Conference on Information Engineering and Computer Science. 2009, 1–4

    Google Scholar 

  11. Jiang D, Lou Y, Jin Y. A revision approach based on assignment equivalence classes. In: Proceedings of the 2nd International Workshop on Intelligent Systems and Applications. 2010, 1–4

    Google Scholar 

  12. Luo J, Li W. An algorithm to compute maximal contractions for Horn clauses. Science in China Series F: Information Sciences, 2011, 54(2): 244–257

    Article  MathSciNet  MATH  Google Scholar 

  13. Nebel B. Belief revision and default reasoning: syntax-based approaches. In: Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning. 1991, 417–428

    Google Scholar 

  14. Nebel B. Base revision operations and schemes: semantics representation, and complexity. In: Proceedings of the 11th European Conference on Artificial Intelligence. 1994, 341–345

    Google Scholar 

  15. Eiter T, Gottlob G. On the complexity of propositional knowledge base revision, updates, and counterfactuals. Artificial Intelligence, 1992, 57(2–3): 227–270

    Article  MathSciNet  MATH  Google Scholar 

  16. Delgrande J P, Schaub T. A consistency-based approach for belief change. Artificial Intelligence, 2003, 151(1): 141

    Article  MathSciNet  Google Scholar 

  17. Delgrande J P, Schaub T, Tompits H, Woltran S. On computing belief change operations using quantified boolean formulas. Journal of Logic and Computation, 2004, 14(6): 426–438

    Article  MathSciNet  Google Scholar 

  18. Gallier J H. Logic for Computer Science: Foundations of Automatic Theorem Proving. New York: Harper & Row, 1985

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jie Luo.

Additional information

Jie Luo received his PhD degree in computer science from Beihang University, Beijing, China, in 2012. His research interests include mathematical logic, formal methods, automated reasoning, and software testing.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Luo, J. A general framework for computing maximal contractions. Front. Comput. Sci. 7, 83–94 (2013). https://doi.org/10.1007/s11704-012-2044-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-012-2044-8

Keywords