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Abstract
The ability of an agent to make quick, rational decisions in
an uncertain environment is paramount for its applicability in realistic
settings. Markov Decision Processes (MDP) provide such a framework,
but can only model uncertainty that can be expressed as probabilities.
Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet
they cannot accurately represent probabilistic sources of uncertainty and
they lack the efficient online solvers found in the probabilistic MDP community.
In this paper we advance the state of the art in three important
ways. Firstly, we propose the first online planner for possibilistic MDP by
adapting the Monte-Carlo Tree Search (MCTS) algorithm. A key component
is the development of efficient search structures to sample possibility
distributions based on the DPY transformation as introduced by
Dubois, Prade, and Yager. Secondly, we introduce a hybrid MDP model
that allows us to express both possibilistic and probabilistic uncertainty,
where the hybrid model is a proper extension of both probabilistic and
possibilistic MDPs. Thirdly, we demonstrate that MCTS algorithms can
readily be applied to solve such hybrid models.
Original language | English |
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Title of host publication | Foundations of Information and Knowledge Systems: 9th International Symposium, FoIKS 2016, Linz, Austria, March 7-11, 2016. Proceedings |
Publisher | Springer-Verlag |
Pages | 24-41 |
ISBN (Electronic) | 978-3-319-30024-5 |
ISBN (Print) | 978-3-319-30023-8 |
DOIs | |
Publication status | Published - 04 Mar 2016 |
Event | 9th International Symposium on Foundations of Information and Knowledge Systems (FoIKS'16) - Linz, Austria Duration: 07 Mar 2016 → 11 Mar 2016 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 9616 |
ISSN (Electronic) | 0302-9743 |
Conference
Conference | 9th International Symposium on Foundations of Information and Knowledge Systems (FoIKS'16) |
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Country/Territory | Austria |
City | Linz |
Period | 07/03/2016 → 11/03/2016 |
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Dive into the research topics of 'Anytime Algorithms for Solving Possibilistic MDPs and Hybrid MDPs'. Together they form a unique fingerprint.Projects
- 1 Finished
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R1070CSC: Providing Autonomous Capabilities for Evolving SCADA (PACES)
Liu, W., Hong, J. & Sezer, S.
01/08/2011 → 31/03/2016
Project: Research