Uncertainty in Artificial Intelligence
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A Possibilistic Model for Qualitative Sequential Decision Problems under Uncertainty in Partially Observable Environments
Regis Sabbadin
Abstract:
In this article we propose a qualitative (ordinal) counterpart for the Partially Observable Markov Decision Processes model (POMDP) in which the uncertainty, as well as the preferences of the agent, are modeled by possibility distributions. This qualitative counterpart of the POMDP model relies on a possibilistic theory of decision under uncertainty, recently developed. One advantage of such a qualitative framework is its ability to escape from the classical obstacle of stochastic POMDPs, in which even with a finite state space, the obtained belief state space of the POMDP is infinite. Instead, in the possibilistic framework even if exponentially larger than the state space, the belief state space remains finite.
Keywords: Possibility theory, multistage decision making, partial observability
Pages: 567-574
PS Link:
PDF Link: /papers/99/p567-sabbadin.pdf
BibTex:
@INPROCEEDINGS{Sabbadin99,
AUTHOR = "Regis Sabbadin ",
TITLE = "A Possibilistic Model for Qualitative Sequential Decision Problems under Uncertainty in Partially Observable Environments",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "567--574"
}


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