000			04164nam a2200577 i 4500
001			7288640
003			IEEE
005			20220712204846.0
006			m o d
007			cr |n|||||||||
008			151229s2015 mauac ob 001 eng d
010			_z 2014048127 (print)
020			_a9780262331708 _qelectronic
020			_z9780262029254 _qhardcover : print
035			_a(CaBNVSL)mat07288640
035			_a(IDAMS)0b00006484a5256a
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aTJ217.5 _b.K63 2015eb
082	0	0	_a003/.56 _223
100	1		_aKochenderfer, Mykel J., _d1980- _924720
245	1	0	_aDecision making under uncertainty : _btheory and application / _cMykel J. Kochenderfer, with contributions from Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds, Jason R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal �Ure, John Vian.
264		1	_aCambridge, Massachusetts : _bMIT Press, _c[2015]
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2015]
300			_a1 PDF (xxv, 323 pages) : _billustrations (some color), portraits.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aLincoln Laboratory series
504			_aIncludes bibliographical references and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aMany important problems involve decision making under uncertainty -- that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance.Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/29/2015.
650		0	_aIntelligent control systems. _93412
650		0	_aAutomatic machinery. _924721
650		0	_aDecision making _xMathematical models. _924722
655		0	_aElectronic books. _93294
695			_aEpitaxial layers
695			_aExcitons
695			_aNitrogen
695			_aRadiative recombination
695			_aSilicon carbide
695			_aTemperature measurement
710	2		_aIEEE Xplore (Online Service), _edistributor. _924723
710	2		_aMIT Press, _epublisher. _924724
776	0	8	_iPrint version: _z9780262029254
830		0	_aLincoln Laboratory series _924617
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=7288640
942			_cEBK
999			_c73439 _d73439