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Lexicographic refinements in stationary possibilistic Markov Decision Processes

Ben Amor, Nahla and El Khalfi, Zeineb and Fargier, Hélène and Sabbadin, Regis Lexicographic refinements in stationary possibilistic Markov Decision Processes. (2018) International Journal of Approximate Reasoning, 103. 343-363. ISSN 0888-613X

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Official URL: https://doi.org/10.1016/j.ijar.2018.10.011


Possibilistic Markov Decision Processes offer a compact and tractable way to represent and solve problems of sequential decision under qualitative uncertainty. Even though appealing for its ability to handle qualitative problems, this model suffers from the drowning effect that is inherent to possibilistic decision theory. The present1 paper proposes to escape the drowning effect by extending to stationary possibilistic MDPs the lexicographic preference relations defined by Fargier and Sabbadin [13] for non-sequential decision problems. We propose a value iteration algorithm and a policy iteration algorithm to compute policies that are optimal for these new criteria. The practical feasibility of these algorithms is then experimented on different samples of possibilistic MDPs.

Item Type:Article
HAL Id:hal-02124080
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Institut National de la Recherche Agronomique - INRA (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université de Tunis (TUNISIA)
Laboratory name:
Deposited On:16 Apr 2019 14:54

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