000 03996nam a22005295i 4500
001 978-3-031-01559-5
003 DE-He213
005 20240730164105.0
007 cr nn 008mamaa
008 220601s2012 sz | s |||| 0|eng d
020 _a9783031015595
_9978-3-031-01559-5
024 7 _a10.1007/978-3-031-01559-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _a, Mausam.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982055
245 1 0 _aPlanning with Markov Decision Processes
_h[electronic resource] :
_bAn AI Perspective /
_cby Mausam , Andrey Kolobov.
250 _a1st ed. 2012.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2012.
300 _aXVI, 194 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_982056
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aKolobov, Andrey.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982057
710 2 _aSpringerLink (Online service)
_982058
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004315
776 0 8 _iPrinted edition:
_z9783031026874
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_982059
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01559-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c85290
_d85290