Logo PTI Logo FedCSIS

Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Multiple Criteria Decision Aiding by Constructive Preference Learning (Keynote Lecture — Extended Abstract)

DOI: http://dx.doi.org/10.15439/2023F6419

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 3939 ()

Full text

Abstract. The notion of preference is relevant across a variety of scientific disciplines, including economics and social sciences, operations research and decision sciences, artificial intelligence, psychology, and philosophy. Preferences provide a means for specifying desires in a declarative and intelligible way, a key element for the effective representation of knowledge and reasoning respecting the value systems of Decision Makers (DMs) [1].

References

  1. E. Hüllermeier and R. Słowiński, “Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies,” Submitted, 2023.
  2. S. Greco, V. Mousseau, and R. Słowiński, “Ordinal regression revisited: multiple criteria ranking using a set of additive value functions,” European Journal of Operational Research, vol. 191, no. 2, pp. 415–435, 2008.
  3. S. Corrente, S. Greco, M. Kadziński, and R. Słowiński, “Robust ordinal regression in preference learning and ranking,” Machine Learning, vol. 93, pp. 381–422, 2013.
  4. S. Corrente, S. Greco, and R. Słowiński, “Multiple criteria hierarchy process in robust ordinal regression,” Decision Support Systems, vol. 53, no. 3, pp. 660–674, 2012.
  5. S. Corrente, S. Greco, B. Matarazzo, and R. Słowiński, “Explainable interactive evolutionary multiobjective optimization,” Omega, p. 102925, 2023.