LEADER 04496nam 22006015 450 001 9910144922503321 005 20200704231135.0 010 $a3-540-69050-6 024 7 $a10.1007/3-540-62934-3 035 $a(CKB)1000000000234638 035 $a(SSID)ssj0000322694 035 $a(PQKBManifestationID)11234027 035 $a(PQKBTitleCode)TC0000322694 035 $a(PQKBWorkID)10288140 035 $a(PQKB)11701511 035 $a(DE-He213)978-3-540-69050-4 035 $a(PPN)15522980X 035 $a(EXLCZ)991000000000234638 100 $a20121227d1997 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aDistributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments$b[electronic resource] $eECAI'96 Workshop LDAIS, Budapest, Hungary, August 13, 1996, ICMAS'96 Workshop LIOME, Kyoto, Japan, December 10, 1996 Selected Papers /$fedited by Gerhard Weiß 205 $a1st ed. 1997. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1997. 215 $a1 online resource (XII, 300 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v1221 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-62934-3 327 $aReader's guide -- Challenges for machine learning in cooperative information systems -- A modular approach to multi-agent reinforcement learning -- Learning real team solutions -- Learning by linear anticipation in multi-agent systems -- Learning coordinated behavior in a continuous environment -- Multi-agent learning with the success-story algorithm -- On the collaborative object search team: a formulation -- Evolution of coordination as a metaphor for learning in multi-agent systems -- Correlating internal parameters and external performance: Learning Soccer Agents -- Learning agents' reliability through Bayesian Conditioning: A simulation experiment -- A study of organizational learning in multiagents systems -- Cooperative Case-based Reasoning -- Contract-net-based learning in a user-adaptive interface agency -- The communication of inductive inferences -- Addressee Learning and Message Interception for communication load reduction in multiple robot environments -- Learning and communication in Multi-Agent Systems -- Investigating the effects of explicit epistemology on a Distributed learning system. 330 $aThe complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information. 410 0$aLecture Notes in Artificial Intelligence ;$v1221 606 $aArtificial intelligence 606 $aComputer simulation 606 $aProgramming languages (Electronic computers) 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 615 0$aArtificial intelligence. 615 0$aComputer simulation. 615 0$aProgramming languages (Electronic computers). 615 14$aArtificial Intelligence. 615 24$aSimulation and Modeling. 615 24$aProgramming Languages, Compilers, Interpreters. 676 $a006.3/1 702 $aWeiß$b Gerhard$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aEuropean Conference on Artificial Intelligence 906 $aBOOK 912 $a9910144922503321 996 $aDistributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments$92225507 997 $aUNINA