[HTML][HTML] Lexicographic refinements in stationary possibilistic Markov Decision Processes

NB Amor, Z El Khalfi, H Fargier, R Sabbadin - International Journal of …, 2018 - Elsevier
International Journal of Approximate Reasoning, 2018Elsevier
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 present 1 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 …
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.
Elsevier
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