Results 31 to 40 of about 13,931 (229)
The Stochastic-Learning-Based Deployment Scheme for Service Function Chain in Access Network
IEEE Access, 2018 The research on topology-aware deployment for service function chain (SFC) is very important to a network slice technique. Nevertheless, most of the existing schemes on topology-aware deployment assume that the network topology information (NTI) is ...
Youchao Yang +4 more
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A Novel Model-Based Reinforcement Learning for Online Anomaly Detection in Smart Power Grid
International Transactions on Electrical Energy Systems, 2023 Smart grids must detect cyber-attacks early to ensure their safety and reliability. There have been many outlier detection methods presented in the studies, varying from those requiring instance-by-instance decisions t the online diagnosing methods that ...
Ling Wang +5 more
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Machine Learning and Knowledge Extraction, 2021 The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) for solving partially observable Markov decision processes (POMDP) problems.
Xuanchen Xiang, Simon Foo, Huanyu Zang
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A POMDP-Based Optimization Method for Sequential Diagnostic Strategy With Unreliable Tests
IEEE Access, 2019 This paper considers the sequential fault diagnosis problem with unreliable tests which exist widely in practice. This problem involves real-time inference of the most likely set of failure sources, i.e., fault state, based on unreliable test outcomes ...
Yajun Liang +5 more
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Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation [PDF]
, 2015 Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable,
Caballero, Fernando +3 more
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POMDP-lite for Robust Robot Planning under Uncertainty
, 2016 The partially observable Markov decision process (POMDP) provides a principled general model for planning under uncertainty. However, solving a general POMDP is computationally intractable in the worst case.
Chen, Min +3 more
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Радіоелектронні і комп'ютерні системиThe subject are mathematical models of decision-making under uncertainty in a production environment with human presence. The research objectives: is to form a safe and effective policy for controlling the motion of a three-link collaborative robot ...
Vladyslav Yevsieiev +3 more
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Online algorithms for POMDPs with continuous state, action, and observation spaces
, 2018 Online solvers for partially observable Markov decision processes have been applied to problems with large discrete state spaces, but continuous state, action, and observation spaces remain a challenge.
Kochenderfer, Mykel, Sunberg, Zachary
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POMDP-based probabilistic decision making for path planning in wheeled mobile robot
Cognitive RoboticsPath Planning in a collaborative mobile robot system has been a research topic for many years. Uncertainty in robot states, actions, and environmental conditions makes finding the optimum path for navigation highly challenging for the robot.
Shripad V. Deshpande +2 more
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Verification of Uncertain POMDPs Using Barrier Certificates
, 2018 We consider a class of partially observable Markov decision processes (POMDPs) with uncertain transition and/or observation probabilities. The uncertainty takes the form of probability intervals.
Ahmadi, Mohamadreza +3 more
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