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UAI
2000
13 years 9 months ago
The Complexity of Decentralized Control of Markov Decision Processes
We consider decentralized control of Markov decision processes and give complexity bounds on the worst-case running time for algorithms that find optimal solutions. Generalization...
Daniel S. Bernstein, Shlomo Zilberstein, Neil Imme...
NFM
2011
225views Formal Methods» more  NFM 2011»
13 years 2 months ago
Synthesis for PCTL in Parametric Markov Decision Processes
Abstract. In parametric Markov Decision Processes (PMDPs), transition probabilities are not fixed, but are given as functions over a set of parameters. A PMDP denotes a family of ...
Ernst Moritz Hahn, Tingting Han, Lijun Zhang
MDAI
2005
Springer
14 years 1 months ago
Perceptive Evaluation for the Optimal Discounted Reward in Markov Decision Processes
We formulate a fuzzy perceptive model for Markov decision processes with discounted payoff in which the perception for transition probabilities is described by fuzzy sets. Our aim...
Masami Kurano, Masami Yasuda, Jun-ichi Nakagami, Y...
AIPS
2011
12 years 11 months ago
Sample-Based Planning for Continuous Action Markov Decision Processes
In this paper, we present a new algorithm that integrates recent advances in solving continuous bandit problems with sample-based rollout methods for planning in Markov Decision P...
Christopher R. Mansley, Ari Weinstein, Michael L. ...
CORR
2008
Springer
154views Education» more  CORR 2008»
13 years 7 months ago
A Counterexample Guided Abstraction-Refinement Framework for Markov Decision Processes
rexample Guided Abstraction-Refinement Framework for Markov Decision Processes ROHIT CHADHA and MAHESH VISWANATHAN Dept. of Computer Science, University of Illinois at Urbana-Champ...
Rohit Chadha, Mahesh Viswanathan