LEADER 04130nam 22006135 450 001 996465862003316 005 20200703042929.0 010 $a3-540-49726-9 024 7 $a10.1007/3-540-60923-7 035 $a(CKB)1000000000234416 035 $a(SSID)ssj0000320806 035 $a(PQKBManifestationID)11229957 035 $a(PQKBTitleCode)TC0000320806 035 $a(PQKBWorkID)10259273 035 $a(PQKB)10410922 035 $a(DE-He213)978-3-540-49726-4 035 $a(PPN)155229958 035 $a(EXLCZ)991000000000234416 100 $a20121227d1996 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdaptation and Learning in Multi-Agent Systems$b[electronic resource] $eIJCAI' 95 Workshop, Montreal, Canada, August 21, 1995. Proceedings. /$fedited by Gerhard Weiß, Sandip Sen 205 $a1st ed. 1996. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1996. 215 $a1 online resource (XII, 568 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v1042 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-60923-7 327 $aAdaptation and learning in multi-agent systems: Some remarks and a bibliography -- Refinement in agent groups -- Opponent modeling in multi-agent systems -- A multi-agent environment for department of defense distribution -- Mutually supervised learning in multiagent systems -- A framework for distributed reinforcement learning -- Evolving behavioral strategies in predators and prey -- To learn or not to learn ...... -- A user-adaptive interface agency for interaction with a virtual environment -- Learning in multi-robot systems -- Learn your opponent's strategy (in polynomial time)! -- Learning to reduce communication cost on task negotiation among multiple autonomous mobile robots -- On multiagent Q-learning in a semi-competitive domain -- Using reciprocity to adapt to others -- Multiagent coordination with learning classifier systems. 330 $aThis book is based on the workshop on Adaptation and Learning in Multi-Agent Systems, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995. The 14 thoroughly reviewed revised papers reflect the whole scope of current aspects in the field: they describe and analyze, both experimentally and theoretically, new learning and adaption approaches for situations in which several agents have to cooperate or compete. Also included, and aimed at the novice reader, are a comprehensive introductory survey on the area with 154 references listed and a subject index. As the first book solely devoted to this area, this volume documents the state of the art and is thus indispensable for anyone active or interested in the field. 410 0$aLecture Notes in Artificial Intelligence ;$v1042 606 $aArtificial intelligence 606 $aProgramming languages (Electronic computers) 606 $aComputer simulation 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 615 0$aArtificial intelligence. 615 0$aProgramming languages (Electronic computers). 615 0$aComputer simulation. 615 14$aArtificial Intelligence. 615 24$aProgramming Languages, Compilers, Interpreters. 615 24$aSimulation and Modeling. 676 $a006.3/1 702 $aWeiß$b Gerhard$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSen$b Sandip$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Joint Conference on Artificial Intelligence 906 $aBOOK 912 $a996465862003316 996 $aAdaptation and Learning in Multi-Agent Systems$92829971 997 $aUNISA