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Journal Articles International Journal of Approximate Reasoning Year : 2018

Lexicographic refinements in stationary possibilistic Markov Decision Processes

Abstract

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.
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Dates and versions

hal-02124080 , version 1 (09-05-2019)

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Nahla Ben Amor, Zeineb El Khalfi, Hélène Fargier, Régis Sabbadin. Lexicographic refinements in stationary possibilistic Markov Decision Processes. International Journal of Approximate Reasoning, 2018, 103 (Virtual special issue on the 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2017)), pp.343-363. ⟨10.1016/j.ijar.2018.10.011⟩. ⟨hal-02124080⟩
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