Partially observable Markov decision process approximations for adaptive sensing

DOI10.1007/s10626-009-0071-xzbMath1192.93111OpenAlexW2164142425MaRDI QIDQ977009

Alfred O. III Hero, Christopher M. Kreucher, Edwin K. P. Chong

Publication date: 16 June 2010

Published in: Discrete Event Dynamic Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10626-009-0071-x

zbMATH Keywords

trackingschedulingMarkov decision processsensingPOMDP

Mathematics Subject Classification ID

Stochastic scheduling theory in operations research (90B36) Markov and semi-Markov decision processes (90C40)

Related Items

Planning for multiple measurement channels in a continuous-state POMDP ⋮ UTS-based foresight optimization of sensor scheduling for low interception risk tracking

Uses Software

• POMDPS
• MCQueue

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Planning and acting in partially observable stochastic domains
• Parallel rollout for online solution of partially observable Markov decision processes
• Sensor management using an active sensing approach
• A POMDP framework for coordinated guidance of autonomous UAVs for multitarget tracking
• Constrained Markov decision processes with total cost criteria: Lagrangian approach and dual linear program
• A new algorithm for stochastic discrete resource allocation optimization
• Rollout algorithms for stochastic scheduling problems
• Nonmyopic sensor scheduling and its efficient implementation for target tracking applications
• A survey of algorithmic methods for partially observed Markov decision processes
• The Linear Programming Approach to Approximate Dynamic Programming
• Computationally Feasible Bounds for Partially Observed Markov Decision Processes
• Using Randomization to Break the Curse of Dimensionality
• The Optimal Control of Partially Observable Markov Processes over a Finite Horizon
• Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes
• Burst-level congestion control using hindsight optimization
• Approximate Dynamic Programming
• On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming