Abstract
Min-based possibilistic influence diagrams offer a compact modeling of decision problems under uncertainty. Uncertainty and preferential relations are expressed on the same structure by using ordinal data. In many applications, it may be natural to represent expert knowledge and preferences separately and treat all nodes similarly. This work shows how an influence diagram can be equivalently represented by two possibilistic networks: the first one represents knowledge of an agent and the second one represents agent’s preferences. Thus, the decision evaluation process is based on more compact possibilistic network. Then, we show that the computation of sequential optimal decisions (strategy) comes down to compute a normalization degree of the junction tree associated with the graph representing the fusion of agents beliefs and its preferences resulting from the proposed decomposition process.
This is an extended and revised version of the conference paper: S. Benferhat, H.F. Khellaf-Haned, I. Zeddigha, “On the Decomposition of Min-Based PIDs”. The 8th conference of Agent and Artificial Intelligence ICAART-2016. Roma, February 2016.
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Acknowledgments
This work has received supports from the french Agence Nationale de la Recherche, ASPIQ project reference ANR-12-BS02-0003. This work has also received support from the european project H2020 Marie Sklodowska-Curie Actions (MSCA) research and Innovation Staff Exchange (RISE): AniAge (High Dimensional Heterogeneous Data based Animation Techniques for Southeast Asian Intangible Cultural Heritage Digital Content), project number 691215.
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Benferhat, S., Boutouhami, K., Khellaf-Haned, H.F., Zeddigha, I. (2017). Qualitative Possibilistic Decisions: Decomposition and Sequential Decisions Making. In: van den Herik, J., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2016. Lecture Notes in Computer Science(), vol 10162. Springer, Cham. https://doi.org/10.1007/978-3-319-53354-4_10
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