LEADER 02950nam 2200493 450 001 9910260645203321 005 20221206093259.0 010 $a0-262-32491-1 035 $a(CKB)2670000000263655 035 $a(CaBNVSL)mat06267472 035 $a(IDAMS)0b000064818b44ac 035 $a(IEEE)6267472 035 $a(EXLCZ)992670000000263655 100 $a20151224d2008 uy 101 0 $aeng 135 $aur|n||||||||| 181 $2rdacontent 182 $2isbdmedia 183 $2rdacarrier 200 10$aLearning in embedded systems /$fby Leslie Pack Kaelbling 210 1$aStanford, Calif. :$cDept. of Computer Science, Stanford University,$d[c1990] 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2008] 215 $a1 PDF (xx, 200 pages) $cillustrations 225 1 $aReport ;$vno. STAN-CS-90-1326 300 $aCover title. 300 $a"June 1990." 311 $a0-262-51278-5 311 $a0-262-28850-8 320 $aIncludes bibliographical references (p. 191-200). 330 $aLearning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University. 410 0$aReport (Stanford University. Computer Science Department) ;$vno. STAN-CS-90-1326. 606 $aEmbedded computer systems$xProgramming 606 $aComputer algorithms 615 0$aEmbedded computer systems$xProgramming. 615 0$aComputer algorithms. 676 $a005.1 700 $aKaelbling$b Leslie Pack$01265122 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910260645203321 996 $aLearning in embedded systems$92966634 997 $aUNINA