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A Partially Observable Markov Decision Process with Lagged Information [PDF]

Journal of the Operational Research Society, 1987
In actuality, we face lots of uncertainty in the application of a Markov process. In order to reduce such uncertainty, it is indispensable to use additional information concerning the state of the process.
Byung Ho Jeong, Soung Hie Kim
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Condition-Based Maintenance for Traction Power Supply Equipment Based on Partially Observable Markov Decision Process

IEEE transactions on intelligent transportation systems (Print), 2022
Actual condition-based maintenance for traction power supply equipment (TPSE) is almost based on completely observable equipment state. However, it is unpractical to accurately reveal the equipment state due to the inescapably uncertainty of state ...
Sheng Lin, Ruidong Fan, D. Feng, Chao Yang, Qi Wang, Shibin Gao   +5 more
semanticscholar   +1 more source

A Machine Learning-Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction

INFORMS journal on computing, 2022
Sepsis is a life-threatening condition, caused by the body’s extreme response to an infection. In the United States, 1.7 million cases of sepsis occur annually, resulting in 265,000 deaths.
Zeyu Liu, A. Khojandi, Xueping Li, Akram Mohammed, Robert L. Davis, Rishikesan Kamaleswaran   +5 more
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Expandable-Partially Observable Markov Decision-Process Framework for Modeling and Analysis of Autonomous Vehicle Behavior

IEEE Systems Journal, 2021
When modeling decision-making in autonomous vehicles (AVs), only a fraction of the required information is usually available at the start due to the presence of uncertainty in the knowledge of the state of the system and the environment.
Parisa Pouya, A. Madni
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Partially Observable Markov Decision Process for Monitoring Multilayer Wafer Fabrication

IEEE Transactions on Automation Science and Engineering, 2021
The properties of a learning-based system are particularly relevant to the process study of the unknown behavior of a system or environment. In the semiconductor industry, there is regularly a partially observable system in which the entire state of the ...
M. Khakifirooz, M. Fathi, C. Chien
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Motion Planning of Mobile Robots in Indoor Topological Environments using Partially Observable Markov Decision Process

IEEE Latin America Transactions, 2021
Deterministic motion planners perform well in simulated environments, where sensors and actuators are perfect. However, these assumptions are restrictive and consequently motion planning will have poor performance if applied to real robotic systems (or a
Neemias Silva Monteiro, V. M. Gonçalves, C. Maia   +2 more
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Energy Management of Fuel Cell Hybrid Vehicle Based on Partially Observable Markov Decision Process

IEEE Transactions on Control Systems Technology, 2020
This paper presents a nonmyopic energy management strategy (EMS) for controlling multiple energy flow in fuel cell hybrid vehicles. The control problem is solved by convex programing under a partially observable Markov decision process-based framework ...
D. Shen, C. Lim, Peng Shi, P. Bujlo
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Partially Observable Markov Decision Processes and Robotics

Annu. Rev. Control. Robotics Auton. Syst., 2022
Planning under uncertainty is critical to robotics. The partially observable Markov decision process (POMDP) is a mathematical framework for such planning problems.
H. Kurniawati
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A Partially Observable Markov Decision Process Approach to Residential Home Energy Management

IEEE Transactions on Smart Grid, 2018
Real-time pricing (RTP) is a utility-offered dynamic pricing program to incentivize customers to make changes in their energy usage. A home energy management system (HEMS) automates the energy usage in a smart home in response to utility pricing signals.
T. Hansen, E. Chong, S. Suryanarayanan, A. A. Maciejewski, H. Siegel   +4 more
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Partially Observable Markov Decision Processes

2011
In many applications the decision maker has only partial information about the state process, i.e. part of the state cannot be observed. Examples can be found in engineering, economics, statistics, speech recognition and learning theory among others. An important financial application is given when the drift of a stock price process is unobservable and
Ulrich Rieder, Nicole Bäuerle
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