Formal models for decision

The ADRIA team conducts in-depth work on qualitative decision criteria under uncertainty and/or multi-criteria, at the confluence of numerical (probabilistic) and symbolic approaches to decision making. Placing ourselves within the general framework of Sugeno’s integrals, we seek to propose generalizations and/or refinements that satisfy the principle of the sure thing co-monotonous.

We are also interested in the problem of representation and treatment of individual or collective preferences on combinatorial (or multi-attribute) domains, such as voting on interrelated variables (e. g. multiple referendums), equitable sharing of indivisible resources, game theory, or reasoning on preference networks, possibly conditional.

The work of the ADRIA team in preference representation also concerns:

  • the use of the language of possibilistic logic (with symbolic weights) as a general framework for representing preferences,
  • the study of a number of variants of the Sugeno integral, including “disintegrales” (which decrease as defects increase), and their logical counterparts,

Recent references

  • Didier Dubois, Hélène Fargier, Agnès Rico. Sugeno Integrals and the Commutation Problem. Dans / In : Modeling Decisions for Artificial Intelligence (MDAI 2018), Torra, Narukawa, Aguilo, Gonzalez-Hidalgo (Eds.), Springer, LNAI 11144, p. 48-63, 2018.
  • Nahla Ben Amor, Hélène Fargier, Régis Sabbadin. Equilibria in Ordinal Games: A Framework based on Possibility Theory . Dans / In  : International Joint Conference on Artificial Intelligence (IJCAI 2017), AAAI Press, p. 105-111, août 2017.
  • Nahla Ben Amor, Fatma Essghaier, Hélène Fargier. Egalitarian collective decision making under qualitative possibilistic uncertainty : Principles and characterization. Dans / In : AAAI Conference on Artificial Intelligence , AAAI Press, p. 3482-3488, janvier 2015.
  • Jérôme Lang, Jérôme Mengin, Lirong Xia. Voting on multi-issue domains with conditionally lexicographic preferences. Dans / In : Artificial Intelligence, Elsevier, Vol. 265, p. 18-44, 2018.