Results 291 to 300 of about 35,395 (320)

Partially observed Markov decision processes (POMDPs)

2016
A POMDP is a controlled HMM. Recall from §2.4 that an HMM consists of an X -state Markov chain { x k } observed via a noisy observation process { y k }. Figure 7.1 displays the schematic setup of a POMDP where the action u k affects the state and/or observation (sensing) process of the HMM.
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Partially Observable Markov Decision Process Approximations for Adaptive Sensing

Discrete Event Dynamic Systems, 2009
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Chong, Edwin K. P., Kreucher, Christopher M., Hero, Alfred O. III   +2 more
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A Non-stationary Infinite Partially-Observable Markov Decision Process

2014
Partially Observable Markov Decision Processes (POMDPs) have been met with great success in planning domains where agents must balance actions that provide knowledge and actions that provide reward. Recently, nonparametric Bayesian methods have been successfully applied to POMDPs to obviate the need of a priori knowledge of the size of the state space,
Kosmopoulos, Dimitrios I., Chatzis, Sotirios P.   +1 more
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Some Monotonicity Results for Partially Observed Markov Decision Processes

Operations Research, 1987
This paper provides sufficient conditions for the optimal value in a discrete-time, finite, partially observed Markov decision process to be monotone on the space of state probability vectors ordered by likelihood ratios. The paper also presents sufficient conditions for the optimal policy to be monotone in a simple machine replacement problem, and ...
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Complexity Bounds for Deterministic Partially Observed Markov Decision Processes

Annals of Operations Research
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Cyrille Vessaire, Pierre Carpentier, Jean-Philippe Chancelier, Michel De Lara, Alejandro Rodríguez-Martínez   +4 more
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Algorithms for partially observable Markov decision processes

2014
Partially Observable Markov Decision Process (POMDP) is a general sequential decision-making model where the effects of actions are nondeterministic and only partial information about world states is available. However, finding near optimal solutions for POMDPs is computationally difficult. Value iteration is a standard algorithm for solving POMDPs. It
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Algorithms for partially observable Markov decision processes

2010
The thesis develops methods to solve discrete-time finite-state partially observable Markov decision processes. For the infinite horizon problem, only discounted reward case is considered. Several new algorithms for the finite horizon and the infinite horizon problems are developed. For the finite horizon problem, two new algorithms are developed.
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Computationally Feasible Bounds for Partially Observed Markov Decision Processes

Operations Research, 1991
A partially observed Markov decision process (POMDP) is a sequential decision problem where information concerning parameters of interest is incomplete, and possible actions include sampling, surveying, or otherwise collecting additional information.
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In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling

Nature Electronics, 2021
Thomas Dalgaty, Damien Querlioz, Elisa Vianello   +2 more
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$\mathcal {H}_{\infty }$ Synchronization for Fuzzy Markov Jump Chaotic Systems With Piecewise-Constant Transition Probabilities Subject to PDT Switching Rule

IEEE Transactions on Fuzzy Systems, 2021
Jing Wang, Jianwei Xia
exaly