Results 21 to 30 of about 13,931 (229)
IEEE Access, 2023 This work presents the method based on the Partially Observable Markov Decision Processes (POMDP) and consensus protocol. The main idea is to share the belief and reach the consensus on the belief state in order to improve local decision making.
Marijana Peti +2 more
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POMDP-BCI: A Benchmark of Active BCI Using POMDP to Issue Commands
IEEE Transactions on Biomedical Engineering, 2023 <p>Past research on Brain-Computer Interfaces (BCI) have presented specific decoding algorithms for each of these modalities. However, some modalities lack efficient decision-making pipelines to treat decoding output, while others have developed highly specific decision-making approaches that are not generalizable to other BCI modalities.
Juan J. Torre Tresols +2 more
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Multi-target detection and recognition by UAVs using online POMDPs [PDF]
, 2013 This paper tackles high-level decision-making techniques for robotic missions, which involve both active sensing and symbolic goal reaching, under uncertain probabilistic environments and strong time constraints.
Lesire, Charles +2 more
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Multi-UAV Mapping and Target Finding in Large, Complex, Partially Observable Environments
Remote Sensing, 2023 Coordinating multiple unmanned aerial vehicles (UAVs) for the purposes of target finding or surveying points of interest in large, complex, and partially observable environments remains an area of exploration.
Violet Walker +2 more
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Prioritizing Point-Based POMDP Solvers [PDF]
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2006 Recent scaling up of partially observable Markov decision process (POMDP) solvers toward realistic applications is largely due to point-based methods that quickly converge to an approximate solution for medium-sized domains. These algorithms compute a value function for a finite reachable set of belief points, using backup operations.
Guy, Shani +2 more
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Qualitative Analysis of POMDPs with Temporal Logic Specifications for Robotics Applications [PDF]
, 2015 We consider partially observable Markov decision processes (POMDPs), that are a standard framework for robotics applications to model uncertainties present in the real world, with temporal logic specifications. All temporal logic specifications in linear-
Chatterjee, Krishnendu +3 more
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Verification of Indefinite-Horizon POMDPs [PDF]
, 2020 The verification problem in MDPs asks whether, for any policy resolving the nondeterminism, the probability that something bad happens is bounded by some given threshold. This verification problem is often overly pessimistic, as the policies it considers may depend on the complete system state.
Bork, Alexander +3 more
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IEEE Access, 2018 In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power.
Tuyen P. Le +2 more
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Machine Learning and Knowledge Extraction, 2021 The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems.
Xuanchen Xiang, Simon Foo
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Bottom-up learning of hierarchical models in a class of deterministic POMDP environments
International Journal of Applied Mathematics and Computer Science, 2015 The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the ...
Itoh Hideaki +3 more
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