000 | 03607nam a22005295i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/3-540-60923-7 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTJ210.2-211.495 | |
072 | 7 |
_aUYQ _2bicssc |
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_aTJFM1 _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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_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. |
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264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c1996. |
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300 |
_aXII, 568 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, _x0302-9743 ; _v1042 |
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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. |
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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 |
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999 |
_c36163 _d36163 |