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007			cr nn 008mamaa
008			220601s2012 sz | o |||| 0|eng d
020			_a9783031015595
024	7		_a10.1007/978-3-031-01559-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ335 _b2012 EB
100	0		_aMausam _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687796
245	1	0	_aPlanning with Markov Decision Processes : _bAn AI Perspective _cby Mausam , Andrey Kolobov
250			_a1st edition 2012
264		1	_aCham _bSpringer International Publishing _c2012
300			_a1 recurso en línea (XVI, 194 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aIntroduction -- MDPs -- Fundamental Algorithms -- Heuristic Search Algorithms -- Symbolic Algorithms -- Approximation Algorithms -- Advanced Notes.
520			_aMarkov 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.
988			_aSynthesis Collection of Technology_2012
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _9668313 _aMarkov, Procesos de
700	1		_aKolobov, Andrey _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687797
776	0	8	_iPrinted edition: _z9783031004315
776	0	8	_iPrinted edition: _z9783031026874
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01559-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eb _zSI