Computing optimal optimistic decisions using min-based possibilistic networks

Possibilistic networks are important and efficient tools for reasoning under uncertainty. This paper proposes a new approach for decision making under uncertainty based on possibilistic networks. The qualitative possibilistic decision is viewed as a data fusion problem of two particular possibilistic networks: the first one encodes agent´s beliefs and the second one represents the qualitative utility. In this framework, we propose a new algorithm for computing optimistic optimal decisions based on merging these two possibilistic networks. We show that the computation of optimal decisions comes down to compute a normalization degree of the junction tree associated with the graph representing the fusion of agent´s beliefs and preferences.