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Título: | Anytime Algorithms for Solving Possibilistic MDPs and Hybrid MDPs |
Autor: | Bauters, Kim; Liu, Weiru; Godo, Lluis CSIC ORCID | Palabras clave: | Markov decision processes Uncertainty analysis Any-time algorithms Model uncertainties |
Fecha de publicación: | 7-mar-2016 | Editor: | Springer Nature | Citación: | 9th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2016; LNAI, 9616, 2016, Pages 24-41. | Resumen: | 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. © Springer International Publishing Switzerland 2016. | URI: | http://hdl.handle.net/10261/155767 | DOI: | 10.1007/978-3-319-30024-5_2 | Identificadores: | doi: 10.1007/978-3-319-30024-5_2 issn: 03029743 isbn: 978-331930023-8 |
Aparece en las colecciones: | (IIIA) Comunicaciones congresos |
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LNAI.9616.FoIKS 2016.p24-41.pdf | 377,57 kB | Unknown | Visualizar/Abrir |
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