Description du livre Etat : New. New! This book is in the same immaculate condition as when it was published 1.05. N� de r�f. du vendeur 353-3031004310-new

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Planning with Markov Decision Processes: An AI Perspective (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Loparo, Kenneth A.; Kolobov, Andrey

Edit� par Springer (2012)

ISBN 10 : 3031004310 ISBN 13 : 9783031004315

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Description du livre Etat : New. N� de r�f. du vendeur I-9783031004315

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Planning with Markov Decision Processes : An AI Perspective

Mausam Natarajan

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ISBN 10 : 3031004310 ISBN 13 : 9783031004315

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Description du livre Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. N� de r�f. du vendeur ria9783031004315_lsuk

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Planning with Markov Decision Processes : An AI Perspective

Natarajan, Mausam

Edit� par Springer 2012-07 (2012)

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Description du livre PF. Etat : New. N� de r�f. du vendeur 6666-IUK-9783031004315

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Planning With Markov Decision Processes : An Ai Perspective

Natarajan, Mausam; Poole, Andrey

Edit� par Springer (2012)

ISBN 10 : 3031004310 ISBN 13 : 9783031004315

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Description du livre Etat : New. N� de r�f. du vendeur 44569072-n

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Planning with Markov Decision Processes

Andrey Kolobov

Edit� par Springer International Publishing Jul 2012 (2012)

ISBN 10 : 3031004310 ISBN 13 : 9783031004315

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Description du livre 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. N� de r�f. du vendeur 9783031004315

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