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001 978-3-540-49726-4
003 DE-He213
005 20160624102045.0
007 cr nn 008mamaa
008 121227s1996 gw | s |||| 0|eng d
020 _a9783540497264
_9978-3-540-49726-4
024 7 _a10.1007/3-540-60923-7
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aAdaption and Learning in Multi-Agent Systems
_h[electronic resource] :
_bIJCAI'95 Workshop Montréal, Canada, August 21, 1995 Proceedings /
_cedited by Gerhard Weiß, Sandip Sen.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c1996.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c1996.
300 _aXII, 568 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science, Lecture Notes in Artificial Intelligence,
_x0302-9743 ;
_v1042
505 0 _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.
520 _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.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 0 _aComputer simulation.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aProgramming Languages, Compilers, Interpreters.
650 2 4 _aSimulation and Modeling.
700 1 _aWeiß, Gerhard.
_eeditor.
700 1 _aSen, Sandip.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540609230
786 _dSpringer
830 0 _aLecture Notes in Computer Science, Lecture Notes in Artificial Intelligence,
_x0302-9743 ;
_v1042
856 4 0 _uhttp://dx.doi.org/10.1007/3-540-60923-7
942 _2EBK6869
_cEBK
999 _c36163
_d36163