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Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : booksXpress, Bayonne, NJ, Etats-Unis
Livre
Soft Cover. Etat : new.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Livre
Etat : New.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Livre
Etat : New.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : GF Books, Inc., Hawthorne, CA, Etats-Unis
Livre
Etat : Fine. Book is in Used-LikeNew condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear. 1.05.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : GF Books, Inc., Hawthorne, CA, Etats-Unis
Livre
Etat : Good. Book is in Used-Good condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain limited notes and highlighting. 1.05.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : Book Deals, Tucson, AZ, Etats-Unis
Livre
Etat : Fine. Like New condition. Great condition, but not exactly fully crisp. The book may have been opened and read, but there are no defects to the book, jacket or pages. 1.05.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : GF Books, Inc., Hawthorne, CA, Etats-Unis
Livre
Etat : Very Good. Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 1.05.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : Book Deals, Tucson, AZ, Etats-Unis
Livre
Etat : Very Good. Very Good condition. Shows only minor signs of wear, and very minimal markings inside (if any). 1.05.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : GF Books, Inc., Hawthorne, CA, Etats-Unis
Livre
Etat : New. Book is in NEW condition. 1.05.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : Book Deals, Tucson, AZ, Etats-Unis
Livre
Etat : New. New! This book is in the same immaculate condition as when it was published 1.05.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : California Books, Miami, FL, Etats-Unis
Livre
Etat : New.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Livre
Etat : As New. Unread book in perfect condition.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Livre impression à la demande
Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Edité par Springer 2012-07, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : Chiron Media, Wallingford, Royaume-Uni
Livre
PF. Etat : New.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : GreatBookPricesUK, Castle Donington, DERBY, Royaume-Uni
Livre
Etat : New.
Edité par Springer International Publishing Jul 2012, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Livre impression à la demande
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes 212 pp. Englisch.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
Livre
Etat : New. 2012. Paperback. . . . . .
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : GreatBookPricesUK, Castle Donington, DERBY, Royaume-Uni
Livre
Etat : As New. Unread book in perfect condition.
Edité par Springer International Publishing, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Livre
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes.
Edité par Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : Kennys Bookstore, Olney, MD, Etats-Unis
Livre
Etat : New. 2012. Paperback. . . . . . Books ship from the US and Ireland.
Edité par Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2012
ISBN 10 : 3031004310ISBN 13 : 9783031004315
Vendeur : moluna, Greven, Allemagne
Livre impression à la demande
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long per.