000 | 03344nam a22005535i 4500 | ||
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001 | 978-3-540-26877-2 | ||
003 | DE-He213 | ||
005 | 20161121230527.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2005 gw | s |||| 0|eng d | ||
020 |
_a9783540268772 _9978-3-540-26877-2 |
||
024 | 7 |
_a10.1007/b138233 _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 |
100 | 1 |
_aHutter, Marcus. _eauthor. |
|
245 | 1 | 0 |
_aUniversal Artificial Intellegence _h[electronic resource] : _bSequential Decisions Based on Algorithmic Probability / _cby Marcus Hutter. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2005. |
|
300 |
_aXX, 278 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 | _aTexts in Theoretical Computer Science An EATCS Series | |
505 | 0 | _aShort Tour Through the Book -- Simplicity & Uncertainty -- Universal Sequence Prediction -- Agents in Known Probabilistics Environments -- The Universal Algorithmic Agent AIXI -- Important Environmental Classes -- Computational Aspects -- Discussion. | |
520 | _aDecision Theory = Probability + Utility Theory + + Universal Induction = Ockham + Bayes + Turing = = A Unified View of Artificial Intelligence This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments. The book introduces these two well-known but very different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an arbitrary unknown environment. Most if not all AI problems can easily be formulated within this theory, which reduces the conceptual problems to pure computational ones. Considered problem classes include sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The discussion includes formal definitions of intelligence order relations, the horizon problem and relations to other approaches to AI. One intention of this book is to excite a broader AI audience about abstract algorithmic information theory concepts, and conversely to inform theorists about exciting applications to AI. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aCoding theory. | |
650 | 0 | _aComputers. | |
650 | 0 | _aMathematical logic. | |
650 | 0 | _aMathematical statistics. | |
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aCoding and Information Theory. |
650 | 2 | 4 | _aTheory of Computation. |
650 | 2 | 4 | _aMathematical Logic and Formal Languages. |
650 | 2 | 4 | _aProbability and Statistics in Computer Science. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540221395 |
830 | 0 | _aTexts in Theoretical Computer Science An EATCS Series | |
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/b138233 |
912 | _aZDB-2-SCS | ||
950 | _aComputer Science (Springer-11645) | ||
999 |
_c500088 _d500088 |