BOUTOUHAMI Khaoula and KHELLAF Faiza, Universite des Sciences et de la Technologie Houari Boumediene, Algeria

Min-based qualitative Possibilistic networks are one of the effective tools for a compact representation of decision problems under uncertainty. The exact approaches for computing decision based on Possibilistic networks are limited by the size of the possibility distributions. Generally, these approaches are based on Possibilistic propagation algorithms. An important step in the computation of the decision is the transformation of the DAG into a secondary structure, known as the junction trees. This transformation is known to be costly and represents a difficult problem. We propose in this paper a new approximate approach for the computation of decision under uncertainty within possibilistic networks. The computing of the optimal optimistic decision no longer goes through the junction tree construction step. Instead, it is performed by calculating the degree of Normalizationin the moral graph resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying its preferences.

Possibilistic decision theory, Min-based possibilistic networks, Moral graph, optimistic criteria.