Reasoning under Uncertainty in the AHP Method using the Belief Function Theory

Amel Ennaceur (1) , Zied Elouedi (1) , Eric Lefevre (2)

1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois

The Analytic Hierarchy Process (AHP) method was introduced to help the decision maker to express judgments on alternatives over a number of criteria. In this paper, our proposal extends the AHP method to an uncertain environment, where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. In fact, we suggest a novel framework that tackles the challenge of introducing uncertainty in both the criterion and the alternative levels, where the objective is to represent imperfection that may appear in the pair-wise comparisons and to model the relationship between these alternatives and criteria through conditional beliefs.

Domaines: Intelligence artificielle [cs.AI]

Amel Ennaceur, Zied Elouedi, Eric Lefevre. Reasoning under Uncertainty in the AHP Method using the Belief Function Theory. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU'2012, Jul 2012, Catania, Italy. pp.373-382, ⟨10.1007/978-3-642-31724-8_39⟩. ⟨hal-03649722⟩