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020 _a9783540446408
_9978-3-540-44640-8
024 7 _a10.1007/3-540-44640-0
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
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_2bicssc
072 7 _aTJFM1
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072 7 _aCOM004000
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_223
245 1 0 _aAdvances in Learning Classifier Systems
_h[electronic resource] :
_bThird International Workshop, IWLCS 2000 Paris, France, September 15–16, 2000 Revised Papers /
_cedited by Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2001.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2001.
300 _aVIII, 280 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,
_x0302-9743 ;
_v1996
505 0 _aTheory -- An Artificial Economy of Post Production Systems -- Simple Markov Models of the Genetic Algorithm in Classifier Systems: Accuracy-Based Fitness -- Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks -- Probability-Enhanced Predictions in the Anticipatory Classifier System -- YACS: Combining Dynamic Programming with Generalization in Classifier Systems -- A Self-Adaptive Classifier System -- What Makes a Problem Hard for XCS? -- Applications -- Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Clinical Database -- Strength and Money: An LCS Approach to Increasing Returns -- Using Classifier Systems as Adaptive Expert Systems for Control -- Mining Oblique Data with XCS -- Advanced Architectures -- A Study on the Evolution of Learning Classifier Systems -- Learning Classifier Systems Meet Multiagent Environments -- The Bibliography -- A Bigger Learning Classifier Systems Bibliography -- An Algorithmic Description of XCS.
520 _aLearning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aMathematical Logic and Formal Languages.
650 2 4 _aComputation by Abstract Devices.
700 1 _aLuca Lanzi, Pier.
_eeditor.
700 1 _aStolzmann, Wolfgang.
_eeditor.
700 1 _aWilson, Stewart W.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540424376
786 _dSpringer
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v1996
856 4 0 _uhttp://dx.doi.org/10.1007/3-540-44640-0
942 _2EBK4934
_cEBK
999 _c34228
_d34228