LEADER 03554nam 2200469 450 001 9910260595903321 005 20221206094231.0 035 $a(CKB)3860000000003480 035 $a(CaBNVSL)mat07288640 035 $a(IDAMS)0b00006484a5256a 035 $a(IEEE)7288640 035 $a(EXLCZ)993860000000003480 100 $a20151229d2015 uy 101 0 $aeng 135 $aur|n||||||||| 181 $2rdacontent 182 $2isbdmedia 183 $2rdacarrier 200 10$aDecision making under uncertainty $etheory and application /$fMykel 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 U?re, John Vian 210 1$aCambridge, Massachusetts :$cMIT Press,$d[2015] 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2015] 215 $a1 PDF (xxv, 323 pages) $cillustrations (some color), portraits 225 1 $aLincoln Laboratory series 311 $a0-262-33170-5 320 $aIncludes bibliographical references and index. 330 $aMany important problems involve decision making under uncertainty -- that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance.Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines. 410 0$aLincoln Laboratory series 606 $aIntelligent control systems 606 $aAutomatic machinery 606 $aDecision making$xMathematical models 615 0$aIntelligent control systems. 615 0$aAutomatic machinery. 615 0$aDecision making$xMathematical models. 676 $a003/.56 700 $aKochenderfer$b Mykel J.$f1980-$0848880 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910260595903321 996 $aDecision making under uncertainty$91895943 997 $aUNINA