Planning for multiple measurement channels in a continuous-state POMDP

DOI10.1007/s10472-013-9361-yzbMath1271.90105OpenAlexW2041410300MaRDI QIDQ360261

Risto Ritala, Mikko Lauri

Publication date: 26 August 2013

Published in: Annals of Mathematics and Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10472-013-9361-y

zbMATH Keywords

dynamic programmingoptimal measurementscontinuous-statepartially observable Markov decision processplanning under uncertainty

Mathematics Subject Classification ID

Dynamic programming (90C39) Reasoning under uncertainty in the context of artificial intelligence (68T37) Markov and semi-Markov decision processes (90C40)

Uses Software

• POMDPS
• PRMLT

Cites Work

• Planning and acting in partially observable stochastic domains
• Planning in partially-observable switching-mode continuous domains
• Partially observable Markov decision process approximations for adaptive sensing
• PRMLT
• Optimal control of Markov processes with incomplete state-information. II: The convexity of the loss-function
• A survey of algorithmic methods for partially observed Markov decision processes
• Spatial Planning: A Configuration Space Approach
• State of the Art—A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms
• The Optimal Control of Partially Observable Markov Processes over a Finite Horizon
• Structured Threshold Policies for Dynamic Sensor Scheduling—A Partially Observed Markov Decision Process Approach
• Solving Continuous-State POMDPs via Density Projection
• Approximate Dynamic Programming
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