A possibilistic graphical model for handling decision problems under uncertainty
Salem Benferhat, Faiza Khellaf, Ismahane Zeddigha
Available Online August 2013.
- https://doi.org/10.2991/eusflat.2013.110How to use a DOI?
- Possibilistic decision theory min-based possibilistic networks junction trees
- 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 are expressed by means of possibility distributions 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Salem Benferhat AU - Faiza Khellaf AU - Ismahane Zeddigha PY - 2013/08 DA - 2013/08 TI - A possibilistic graphical model for handling decision problems under uncertainty BT - 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2013.110 DO - https://doi.org/10.2991/eusflat.2013.110 ID - Benferhat2013/08 ER -