| Record ID | marc_loc_updates/v39.i52.records.utf8:51478294:1346 |
| Source | Library of Congress |
| Download Link | /show-records/marc_loc_updates/v39.i52.records.utf8:51478294:1346?format=raw |
LEADER: 01346cam a22003134a 4500
001 2010054421
003 DLC
005 20111220101413.0
008 110128s2012 paua b 001 0 eng
010 $a 2010054421
020 $a9781609601652 (hardcover)
020 $a1609601653 (hardcover)
020 $a9781609601676 (ebook)
020 $a160960167X (ebook)
035 $a(OCoLC)ocn617382246
040 $aDLC$cDLC$dYDX$dBTCTA$dYDXCP$dDLC
042 $apcc
050 00 $aQ335$b.D43 2012
082 00 $a006.3$222
245 00 $aDecision theory models for applications in artificial intelligence :$bconcepts and solutions /$c[edited by] L. Enrique Sucar, Eduardo F. Morales, Jesse Hoey.
260 $aHershey, PA :$bInformation Science Reference,$cc2012.
300 $axiv, 428 p. :$bill. ;$c29 cm.
520 $a"This book provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence"--$cProvided by publisher.
504 $aIncludes bibliographical references (p. 385-416) and index.
650 0 $aArtificial intelligence$xStatistical methods.
650 0 $aBayesian statistical decision theory.
700 1 $aSucar, L. Enrique,$d1957-
700 1 $aMorales, Eduardo F.
700 1 $aHoey, Jesse.