Neutrosophic Logic Applied to Decision Making

  • Henrik Madsen
  • Grigore Albeanu
  • Bernard Burtschy
  • Florin Popentiu-Vladicescu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 308)


Decision making addresses the usage of various methods to select “the best”, in some way, alternative strategy (from many available) when a problem is given for solving. The authors propose the usage of neutrosophic way of thinking, called also Smarandache’s logic, to select a model by experts when degrees of trustability, ultrastability (falsehood), and indeterminacy are used to decide. The procedures deal with multi-attribute neutrosophic decision making and a case study on e-learning software objects is presented.


Neutrosophic sets Decision making e-learning 



During this research, the authors were supported by their departments according to the institutional research strategy and associated research programs.


  1. 1.
    Albeanu, G., Popentiu-Vladicescu, F.: Recent soft computing approaches in digital learning object evaluation. In: 8th International Scientific Conference eLearning and Software for Education, pp. 16–21 (2012)Google Scholar
  2. 2.
    Albeanu, G., Popentiu-Vladicescu, F.: On designing learning objects for a software reliability engineering course. In: 7th International Scientific Conference eLearning and Software for Education, pp. 105–110Google Scholar
  3. 3.
    Albeanu, G., Duda, I.G.: Intuitionistic fuzzy approaches for quality evaluation of learning objects. In: 17th ISSAT International Conference on Reliability and Quality in Design, International Society of Science and Applied Technologies, pp. 258–262 (2011)Google Scholar
  4. 4.
    Atanassov, K.T.: Intuitionistic fuzzy sets. Physica-Verlag, Heidelberg (1999)CrossRefMATHGoogle Scholar
  5. 5.
    Brotherton, M.D., Huynh-Thu Q., Hands D.S., Brunnström K.: Subjective multimedia quality assessment. IEICE Trans. Fundam. E89(A 11) 2920–2932 (2006)Google Scholar
  6. 6.
    Hung, W.-L., Yang, M.-S.: On similarity measures between intuitionistic fuzzy sets. Int. J. Intell. Syst. 23, 364–383 (2008)CrossRefMATHGoogle Scholar
  7. 7.
    Kandasamy, V.W.B., Smarandache, F., Ilanthenral, K.: Social fuzzy matrices for socials scientists. (2007)
  8. 8.
    Mich, L., Fedrizzi, M., Gaio L.: Approximate reasoning in the modeling of consensus in group decisions. In: Klement, E.P., Slany., W. (eds.) Fuzzy logic in artificial intelligence, pp. 91–102 (Springer, Heidelberg 1993)Google Scholar
  9. 9.
    Morales, M.E.M., Gómez, A.D.A., García, P.F.J., Therón S.R.: Supporting the quality of learning objects through their ranking visualization, iJET—4 (Special Issue 1) “SIIE’2008”, pp. 24–29 (2009)Google Scholar
  10. 10.
    Popentiu-Vladicescu, F.: Software reliability engineering. Course book of series of advanced mechatronics systems (Debrecen 2012)Google Scholar
  11. 11.
    Popentiu-Vladicescu, F.: Home page of course 02445 software reliability. (2013)
  12. 12.
    Smarandache, F.: A unified field in logics: neutrosophic logic. American Research Press, Rehoboth (1995)Google Scholar
  13. 13.
    Smarandache, F.: Neutrosophy, neutrosophic logic, neutrosophic set, neutrosophic probability and statistics.
  14. 14.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Henrik Madsen
    • 1
  • Grigore Albeanu
    • 2
  • Bernard Burtschy
    • 3
  • Florin Popentiu-Vladicescu
    • 4
  1. 1.Department of Informatics and Mathematical ModellingTechnical University of DenmarkLyngbyDenmark
  2. 2.Department of Mathematics and Computer ScienceSpiru Haret UniversityBucharestRomania
  3. 3.Telecom ParisTech, Informatique et RéseauxParis Cedex 13France
  4. 4.“UNESCO” DepartmentUniversity of OradeaOradeaRomania

Personalised recommendations