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Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11807 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
Pointbased value iteration: An anytime algorithm for POMDPs
, 2003
"... This paper introduces the PointBased Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points, and planning for those only. By using stochastic trajectories to choose belief points, and by ..."
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Cited by 345 (26 self)
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This paper introduces the PointBased Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points, and planning for those only. By using stochastic trajectories to choose belief points
Value iteration
 25 Years of Model Checking
, 2008
"... Abstract. We survey value iteration algorithms on graphs. Such algorithms can be used for determining the existence of certain paths (model checking), the existence of certain strategies (game solving), and the probabilities of certain events (performance analysis). We classify the algorithms acco ..."
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Cited by 4 (0 self)
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Abstract. We survey value iteration algorithms on graphs. Such algorithms can be used for determining the existence of certain paths (model checking), the existence of certain strategies (game solving), and the probabilities of certain events (performance analysis). We classify the al
Value Iteration for
"... We present a value iteration algorithm for learning to act in Partially Observable Markov Decision Processes (POMDPs) with continuous state spaces. Mainstream POMDP research focuses on the discrete case and this complicates its application to, e.g., robotic problems that are naturally modeled using ..."
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We present a value iteration algorithm for learning to act in Partially Observable Markov Decision Processes (POMDPs) with continuous state spaces. Mainstream POMDP research focuses on the discrete case and this complicates its application to, e.g., robotic problems that are naturally modeled using
Heuristic search value iteration for pomdps
 In UAI
, 2004
"... We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI). HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two wellknown techniques: attentionfocusing search ..."
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Cited by 139 (4 self)
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We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI). HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two wellknown techniques: attention
Factored value iteration converges
 Acta Cyb
"... Abstract. In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solution of factored Markov decision processes (fMDPs). The traditional approximate value iteration algorithm is modified in two ways. For one, the leastsquares projection operator is modified ..."
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Cited by 12 (2 self)
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Abstract. In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solution of factored Markov decision processes (fMDPs). The traditional approximate value iteration algorithm is modified in two ways. For one, the leastsquares projection operator is modified
Horizonbased Value Iteration
"... We present a horizonbased value iteration algorithm called Reverse Value Iteration (RVI). Empirical results on a variety of domains, both synthetic and real, show RVI often yields speedups of several orders of magnitude. RVI does this by ordering backups by horizons, with preference given to clos ..."
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We present a horizonbased value iteration algorithm called Reverse Value Iteration (RVI). Empirical results on a variety of domains, both synthetic and real, show RVI often yields speedups of several orders of magnitude. RVI does this by ordering backups by horizons, with preference given
Perseus: Randomized pointbased value iteration for POMDPs
 Journal of Artificial Intelligence Research
, 2005
"... Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Pointbased approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a ra ..."
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Cited by 202 (16 self)
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randomized pointbased value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary
Focused Topological Value Iteration
, 2009
"... Topological value iteration (TVI) is an effective algorithm for solving Markov decision processes (MDPs) optimally, which 1) divides an MDP into stronglyconnected components, and 2) solves these components sequentially. Yet, TVI’s usefulness tends to degrade if an MDP has large components, because ..."
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Cited by 4 (1 self)
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Topological value iteration (TVI) is an effective algorithm for solving Markov decision processes (MDPs) optimally, which 1) divides an MDP into stronglyconnected components, and 2) solves these components sequentially. Yet, TVI’s usefulness tends to degrade if an MDP has large components, because
Topological Value Iteration Algorithms
"... Value iteration is a powerful yet inefficient algorithm for Markov decision processes (MDPs) because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed. Amon ..."
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Cited by 4 (0 self)
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Value iteration is a powerful yet inefficient algorithm for Markov decision processes (MDPs) because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed
Results 1  10
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1,075,831