Partially Observed Markov Decision Processes: From Filtering to Controlled Sensing [Bookshelf]
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DOI: 10.1109/mcs.2019.2913493
Abstract: Optimal decision making under uncertainty is of increasing importance in artificial intelligence, machine learning, signal processing, and control. Partially observed Markov decision processes (POMDPs) are a significant paradigm in real-world sequential decision making. The framework… read more here.
Keywords: markov decision; partially observed; observed markov; controlled sensing ... See more keywords