000 | 02907nam a22004695i 4500 | ||
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001 | 978-3-319-02606-0 | ||
003 | DE-He213 | ||
005 | 20200421112227.0 | ||
007 | cr nn 008mamaa | ||
008 | 130930s2014 gw | s |||| 0|eng d | ||
020 |
_a9783319026060 _9978-3-319-02606-0 |
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024 | 7 |
_a10.1007/978-3-319-02606-0 _2doi |
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050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aChakraborty, Doran. _eauthor. |
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245 | 1 | 0 |
_aSample Efficient Multiagent Learning in the Presence of Markovian Agents _h[electronic resource] / _cby Doran Chakraborty. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014. |
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300 |
_aXVIII, 147 p. 31 illus. _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 |
_aStudies in Computational Intelligence, _x1860-949X ; _v523 |
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505 | 0 | _aIntroduction -- Background -- Learn or Exploit in Adversary Induced Markov Decision Processes -- Convergence, Targeted Optimality and Safety in Multiagent Learning -- Maximizing -- Targeted Modeling of Markovian agents -- Structure Learning in Factored MDPs -- Related Work -- Conclusion and Future Work. | |
520 | _aThe problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games (a.k.a. normal form games). The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved. In particular this book deals with learning in the presence of a new class of agent behavior that has not been studied or modeled before in a MAL context: Markovian agent behavior. Several new challenges arise when interacting with this particular class of agents. The book takes a series of steps towards building completely autonomous learning algorithms that maximize utility while interacting with such agents. Each algorithm is meticulously specified with a thorough formal treatment that elucidates its key theoretical properties. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319026053 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v523 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-02606-0 |
912 | _aZDB-2-ENG | ||
942 | _cEBK | ||
999 |
_c57770 _d57770 |