000 03344nam a22005535i 4500
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.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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