LEADER 01632nam--2200469---450- 001 990001245970203316 005 20091014122647.0 010 $a0-941320-1 035 $a000124597 035 $aUSA01000124597 035 $a(ALEPH)000124597USA01 035 $a000124597 100 $a20031106d1995----km-y0enga50------ba 101 $aeng 102 $aUS 105 $ay|||z|||101yy 200 1 $a<> united nations compensation commission$ethirteenth Sokol Colloquium$fedited by Richard B. Lillich 210 $aIrvington$cTransnational publishers$d1995 215 $aXI, 486 p.$d24 cm 410 0$12001 454 1$12001 461 1$1001-------$12001 606 0 $aDanni di guerra$xGuerra del Golfo Persico$xRisarcimento$xDritto internazionale 606 0 $aNazioni Unite$xCommissione per i risarcimenti 676 $a956.704421 702 1$aLILLICH,$bRichard B. 710 12$aSokol Colloquium$d<13. ;$eCharlottesville:$f1995>$0556734 801 0$aIT$bsalbc$gISBD 912 $a990001245970203316 951 $aXXIII.2.B. 115 (IG VIII 8 667)$b37358 G.$cXXIII.2.B. 115 (IG VIII 8)$d00087840 959 $aBK 969 $aGIU 979 $aMARIA$b10$c20031106$lUSA01$h1155 979 $aPATRY$b90$c20040406$lUSA01$h1729 979 $aFIORELLA$b90$c20041117$lUSA01$h1109 979 $aFIORELLA$b90$c20041117$lUSA01$h1118 979 $aFIORELLA$b90$c20041117$lUSA01$h1119 979 $aFIORELLA$b90$c20041117$lUSA01$h1120 979 $aANGELA$b90$c20041122$lUSA01$h1458 979 $aRSIAV2$b90$c20091014$lUSA01$h1226 996 $aUnited nations compensation commission$9987996 997 $aUNISA LEADER 05688nam 2200601Ia 450 001 9910437572403321 005 20200520144314.0 010 $a9783642323751 010 $a3642323758 024 7 $a10.1007/978-3-642-32375-1 035 $a(CKB)2670000000342694 035 $a(EBL)1030782 035 $a(OCoLC)836407052 035 $a(SSID)ssj0000879138 035 $a(PQKBManifestationID)11540466 035 $a(PQKBTitleCode)TC0000879138 035 $a(PQKBWorkID)10837661 035 $a(PQKB)11077210 035 $a(DE-He213)978-3-642-32375-1 035 $a(MiAaPQ)EBC1030782 035 $a(PPN)169138046 035 $a(EXLCZ)992670000000342694 100 $a20130110d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aIntrinsically motivated learning in natural and artificial systems /$fedited by Gianluca Baldassarre, Marco Mirolli 205 $a2nd ed. 210 $aNew York $cSpringer$d2013 215 $a1 online resource (454 p.) 300 $aDescription based upon print version of record. 311 08$a9783642442933 311 08$a3642442935 311 08$a9783642323744 311 08$a364232374X 327 $aChap. 1 - Intrinsically Motivated Learning Systems: An Overview -- Chap. 2 - Intrinsic Motivation and Reinforcement Learning -- Chap. 3 - Functions and Mechanisms of Intrinsic Motivations -- Chap. 4 - Exploration from Generalization Mediated by Multiple Controllers -- Chap. 5 - Maximizing Fun by Creating Data with Easily Reducible Subjective Complexity -- Chap. 6 - The Role of the Basal Ganglia in Discovering Novel Actions -- Chap. 7 - Action Discovery and Intrinsic Motivation: A Biologically Constrained Formalisation -- Chap 8 - Novelty Detection as an Intrinsic Motivation for Cumulative Learning Robots -- Chap. 9 - Novelty and Beyond: Towards Combined Motivation Models and Integrated Learning Architectures -- Chap. 10 - The Hippocampal-VTA Loop: The Role of Novelty and Motivation in Controlling the Entry of Information into Long-Term Memory -- Chap. 11 - Deciding Which Skill to Learn When: Temporal-Di?erence Competence-Based Intrinsic Motivation (TD-CB-IM) -- Chap. 12 - Intrinsically Motivated Affordance Discovery and Modeling -- Chap. 13 - Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints -- Chap. 14 - Investigating the Origins of Intrinsic Motivation in Human Infants -- Chap. 15 - A Novel Behavioural Task for Researching Intrinsic Motivations -- Chap. 16 - The ?Mechatronic Board?: A Tool to Study Intrinsic Motivations in Humans, Monkeys, and Humanoid Robots -- Chap. 17 - The iCub Platform: A Tool for Studying Intrinsically Motivated Learning. 330 $aIt has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement. To learn autonomously and in a cumulative fashion is one of the hallmarks of intelligence, and we know that higher mammals engage in exploratory activities that are not directed to pursue goals of immediate relevance for survival and reproduction but are instead driven by intrinsic motivations such as curiosity, interest in novel stimuli or surprising events, and inter­est in learning new behaviours. The adaptive value of such intrinsically motivated activities lies in the fact that they allow the cumulative acquisition of knowledge and skills that can be used later to accomplish ?tness-enhanc­ing goals. Intrinsic motivations continue during adulthood, and in humans they underlie lifelong learning, artistic creativity, and scientific discovery, while they are also the basis for processes that strongly affect human well-being, such as the sense of competence, self-determination, and self-esteem. This book has two aims: to present the state of the art in research on intrinsically motivated learning, and to identify the related scientific and technological open challenges and most promising research directions. The book introduces the concept of intrinsic motivation in artificial systems, reviews the relevant literature, offers insights from the neural and behavioural sciences, and presents novel tools for research. The book is organized into six parts: the chapters in Part I give general overviews on the concept of intrinsic motivations, their function, and possible mechanisms for implementing them; Parts II, III, and IV focus on three classes of intrinsic motivation mechanisms, those based on predictors, on novelty, and on competence; Part V discusses mechanisms that are complementary to intrinsic motivations; and Part VI introduces tools and experimental frameworks for investigating intrinsic motivations. The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots. 606 $aRobots 606 $aIntrinsic motivation 615 0$aRobots. 615 0$aIntrinsic motivation. 676 $a629.892 701 $aBaldassarre$b Gianluca$01752325 701 $aMirolli$b Marco$01752326 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437572403321 996 $aIntrinsically motivated learning in natural and artificial systems$94187602 997 $aUNINA