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101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 00$aMarkov decision processes in artificial intelligence $eMDPs, beyond MDPs and applications /$fedited by Olivier Sigaud, Olivier Buffet
210   $aLondon $cISTE$d[2010]
215   $a1 online resource (457 pages)
300   $aFirst published 2008 in France by Hermes Science/Lavoisier in two volumes entitled: Processus de?cisionnels de Markov en intelligence artificielle.
311   $a1-84821-167-8 
320   $aIncludes bibliographical references and index.
327   $apt. 1. MDPs : models and methods -- pt. 2. Beyond MDPs -- pt. 3. Applications.
330   $aMarkov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustr
410  0$aSafari tech books online.
410  0$aWiley UBCM ebooks.
410  0$aISTE
606   $aArtificial intelligence$xMathematics
606   $aArtificial intelligence$xStatistical methods
606   $aMarkov processes
606   $aStatistical decision
615  0$aArtificial intelligence$xMathematics.
615  0$aArtificial intelligence$xStatistical methods.
615  0$aMarkov processes.
615  0$aStatistical decision.
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676   $a006.301509233
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700   $aSigaud$b Olivier$0564768
701   $aSigaud$b Olivier$0564768
701   $aBuffet$b Olivier$0953653
801  0$bMiAaPQ
801  1$bMiAaPQ
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906   $aBOOK
912   $a9910830115603321
996   $aMarkov decision processes in artificial intelligence$92156329
997   $aUNINA