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Record Nr. |
UNINA9910830115603321 |
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Autore |
Sigaud Olivier |
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Titolo |
Markov decision processes in artificial intelligence : MDPs, beyond MDPs and applications / / edited by Olivier Sigaud, Olivier Buffet |
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Pubbl/distr/stampa |
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ISBN |
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1-118-62010-0 |
1-118-55742-5 |
1-299-31547-X |
1-118-61987-0 |
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Descrizione fisica |
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1 online resource (457 pages) |
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Altri autori (Persone) |
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SigaudOlivier |
BuffetOlivier |
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Disciplina |
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006.301/509233 |
006.301509233 |
006.33 |
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Soggetti |
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Artificial intelligence - Mathematics |
Artificial intelligence - Statistical methods |
Markov processes |
Statistical decision |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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First published 2008 in France by Hermes Science/Lavoisier in two volumes entitled: Processus deĢcisionnels de Markov en intelligence artificielle. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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pt. 1. MDPs : models and methods -- pt. 2. Beyond MDPs -- pt. 3. Applications. |
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Sommario/riassunto |
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Markov 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 |
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