Explainable Decisions under Incomplete Knowledge with Supports and Weights

Florence Dupin de Saint-Cyr (1, 2) , Romain Guillaume (1, 3) , Umer Mushtaq (4, 5)

1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
2 UT3 - Université Toulouse III - Paul Sabatier
3 UT2J - Université Toulouse - Jean Jaurès
4 LabEx MME-DII
5 LEMMA - Laboratoire d'économie mathématique et de microéconomie appliquée

Our research concerns the problem of explainable decision in a context of incomplete knowledge. We define a framework called Bipolar Layered Framework with Support and Weights (BLFSW) that represents the set of argument graphs that can be used in the domain, enabling us to compute what results can be obtained in the different decision situations. This framework also contains information about the utilities/disutilities of these tangible results. This paper extends Bipolar Layered Frameworks defined in [1] by enabling the expression of supports for decision principles and by giving the user the possibility to fix the strength of inhibitors and supports with weights. This increased expressiveness of the framework is important both for refining the evaluation of alternatives and to improve the compactness of the representation. The main result of this paper is to provide an automatic way to explain a possibilistic decision setting in terms of a BLFSW which makes explicit the principles that govern the decision.

Mots clés: Possibility theory; qualitative decision theory; explanation

Domaines: Intelligence artificielle [cs.AI]; Sciences cognitives

Florence Dupin de Saint-Cyr, Romain Guillaume, Umer Mushtaq. Explainable Decisions under Incomplete Knowledge with Supports and Weights. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2019), IEEE : Institute of Electrical and Electronics Engineers, Jun 2019, New Orleans, Lousiana, United States. pp.1-14, ⟨10.1109/FUZZ-IEEE.2019.8858932⟩. ⟨hal-03325774⟩