A generic framework to include Belief functions in preference handling and multi-criteria decision

Un cadre général pour inclure les fonctions de croyance dans le traitement des préférences multi-critères

S. Destercke (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

Modelling the preferences of a decision maker about alternatives having multiple criteria usually starts by collecting preference information (comparisons of alternatives, importance of criteria,. . .), which are then used to fit a preference model issued from some set of hypothesis (weighted average, CP-net, lexicographic orderings,. . .). In practice, this process may often lead to inconsistencies that may be due to inaccurate information provided by the decision maker, who can be unsure about the provided information, or to a poor choice of hypothesis set, which can be too restrictive or not well adapted to the decision process. In this paper, we propose to use belief functions as a way to quantify and resolve such inconsistencies, notably by allowing the decision maker to express her/his certainty about the provided preferential information. Our framework is generic, in the sense that it does not assume a given set of hypothesis a priori, and is consistent with precise methods, in the sense that in the absence of uncertainty and inconsistencies in the information, precise models are ultimately retrieved.

Mots clés: lexicographic orders; evidence theory; AHP; multi-criteria; preferences; inconsistency handling; uncertainty

Domaines: Intelligence artificielle [cs.AI]

S. Destercke. A generic framework to include Belief functions in preference handling and multi-criteria decision. International Journal of Approximate Reasoning, 2018, 98, pp.62-77. ⟨10.1016/j.ijar.2018.04.005⟩. ⟨hal-01781327⟩