Central Library

Planning with Markov Decision Processes (Record no. 85290)

000 -LEADER
fixed length control field 03996nam a22005295i 4500
001 - CONTROL NUMBER
control field 978-3-031-01559-5
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730164105.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2012 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031015595
-- 978-3-031-01559-5
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author , Mausam.
245 10 - TITLE STATEMENT
Title Planning with Markov Decision Processes
Sub Title An AI Perspective /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2012.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVI, 194 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Artificial Intelligence and Machine Learning,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- MDPs -- Fundamental Algorithms -- Heuristic Search Algorithms -- Symbolic Algorithms -- Approximation Algorithms -- Advanced Notes.
520 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Kolobov, Andrey.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01559-5
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2012.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine learning.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Neural networks (Computer science) .
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine Learning.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Mathematical Models of Cognitive Processes and Neural Networks.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1939-4616
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-- ZDB-2-SXSC

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