Skip to main content
SpringerLink
Log in
Book cover

Conference of the Spanish Association for Artificial Intelligence

CAEPIA 2018: Advances in Artificial Intelligence pp 174–183Cite as

Fuzzy Information and Contexts for Designing Automatic Decision-Making Systems

  • Conference paper
  • First Online:

  • 841 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11160))

Abstract

The replacement of people by Automatic Decision-making Systems (ADS) has become a threat today. However, it seems that this replacement is unstoppable. Thus, the need for future and current ADS to perform their tasks as perfectly as possible is, more than a necessity an obligation. Hence, the design of these ADS must be carried out in accordance with the theoretical models on which they are to be built. From this point of view, this paper considers the classic definition of General Decision Making Problem and introduces two new key elements for building ADS: the nature of the information available and the context in which the problem is being solved. The new definition allows to cover different models and decision and optimization problems, some of which are presented for illustrative purposes.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bammer, G., Smithson, M.: The nature of uncertainty. In: Bammer, G., Smithson, M. (eds.) Uncertainty and Risk: Multidisciplinary Perspectives, pp. 289–303. Earthscan (2009)

    Google Scholar 

  2. Brundtland Report: Our Common Future. United Nations (1987)

    Google Scholar 

  3. Ebrahimnejad, A., Verdegay, J.L.: Fuzzy Sets-Based Methods and Techniques for Modern Analytics. Studies in Fuzziness and Soft Computing, vol. 364. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73903-8

    Book  MATH  Google Scholar 

  4. European Recommendations on Machine-Learned Automated Decision Making: When Computers Decide. Informatics Europe & EUACM (2018)

    Google Scholar 

  5. Cebrián, J.L.: The estate of the planet. EL PAÍS, 22 April 2018. (in Spanish)

    Google Scholar 

  6. Hejase, H.J., Hamdar, B., Hashem, F., Bou, S.R.: Decision making under stress: an exploratory study in lebanon. MENA Sci. 3(12), 1–16 (2017)

    Google Scholar 

  7. http://www.huawei.com/es/press-events/news

  8. Lamata, M.T., Pelta, D.A., Verdegay, J.L.: Optimisation Problems as Decision Problems: The case of fuzzy Optimisation Problems. Inf. Sci. 460, 377–388 (2018). https://doi.org/10.1016/j.ins.2017.07.035

    Article  MathSciNet  Google Scholar 

  9. Lamata, M.T., Verdegay, J.L.: On new frameworks for decision making and optimization. In: Gil, E., Gil, E., Gil, J., Gil, M.Á. (eds.) The Mathematics of the Uncertain. SSDC, vol. 142, pp. 629–641. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73848-2_58

    Chapter  Google Scholar 

  10. Lamata, M.T, Pelta, D.A., Verdegay, J.L.: The role of the context in decision and optimization problems. In: Marsala, C., Lesot, M.J. (eds.) A Fuzzy Dictionary of Fuzzy Modelling. Studies in Fuzziness and Soft Computing. Springer, Heidelberg (in press)

    Google Scholar 

  11. Lin, P., Abney, K., Jenkins, R.: Robot Ethics 2.0 (From Autonomous Cars to Artificial Intelligence). Oxford University Press, Oxford (2017)

    Google Scholar 

  12. Lindley, D.V.: Making Decisions. Wiley, New York (1971)

    Google Scholar 

  13. Observatory of CSR. http://observatoriorsc.org/la-rsc-que-es/

  14. Rios, S.: Decision analysis. ICE Ediciones, Madrid (1976). (in Spanish)

    Google Scholar 

  15. Smithson, M.: Ignorance and Uncertainty: Emerging Paradigms. Springer, New York (1989). https://doi.org/10.1007/978-1-4612-3628-3

    Book  Google Scholar 

  16. Thaler, R., Sunstein, C.: Nudge, Improving decisions about health, wealth and happiness. Yale University Press, New Haven (2008)

    Google Scholar 

  17. Verdegay, J.L.: Fuzzy Sets based Heuristics for Optimization. Studies in Fuzziness and Soft Computing, vol. 126. Springer, New York (2010)

    MATH  Google Scholar 

  18. Von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Wiley, New York (1944)

    MATH  Google Scholar 

  19. Yankelevich, D. https://www.linkedin.com/pulse/prediciendo-un-futuro-predicho-daniel-yankelevich?trk=hp-feed-article-title-like

  20. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

Download references

Acknowledgement

This paper has been partially supported by the projects TIN2014-55024-P and TIN2017-86647-P (both including FEDER funds) from the Spanish Ministry of Economy and Competitiveness.

Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, Universidad de Granada, 18014, Granada, Spain

    Maria Teresa Lamata, David A. Pelta & José Luis Verdegay

Authors
  1. Maria Teresa Lamata
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David A. Pelta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Luis Verdegay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Luis Verdegay .

Editor information

Editors and Affiliations

  1. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Francisco Herrera

  2. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Damas

  3. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Rosana Montes

  4. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Alonso

  5. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Óscar Cordón

  6. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Antonio González

  7. School of Engineering, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lamata, M.T., Pelta, D.A., Verdegay, J.L. (2018). Fuzzy Information and Contexts for Designing Automatic Decision-Making Systems. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00374-6_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00373-9

  • Online ISBN: 978-3-030-00374-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation