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A possibilistic graphical model for handling decision problems under uncertainty

1. Abstract Possibilistic networks are important and efficient tools for reasoning under uncertainty. This paper proposes a new graphical model for decision making under uncertainty based on possibilistic networks. In possibilistic decision problems under uncertainty, available knowledge is expressed by means of possibility distribution and preferences are encoded by means another possibility distribution representing the qualitative utility. The first part of the paper proposes a new graphical way to represent such problem, where agent’s knowledge and preferences are encoded separately by two distinct possibilistic networks. The first one encodes agent’s beliefs and the second one represents the qualitative utility. The second part of the paper proposes a new algorithm for computing optimistic optimal decisions based on merging these two possibilistic networks. In fact, the qualitative possibilistic decision is viewed as a data fusion problem of these two particular 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.