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  • 1
    Online Resource
    Online Resource
    From animals to animats 2 : proceedings of the Second International Conference on Simulation of Adaptive Behavior (1993)
    Cambridge, Mass. :MIT Press,
    Details
    UID:
    almahu_9949253368902882
    Format: 1 online resource (x, 523 pages) : , illustrations.
    ISBN: 9780262287159 , 0262287153 , 0585038554 , 9780585038551
    Series Statement: Complex adaptive systems
    Content: More than sixty contributions in From Animals to Animats2 by researchers in ethology, ecology, cybernetics, artificial intelligence, robotics, and related fields investigate behaviors and the underlying mechanisms that allow animals and, potentially, robots to adapt and survive in uncertain environments. Jean-Arcady Meyer is Director of Research, CNRS, Paris. Herbert L. Roitblat is Professor of Psychology at the University of Hawaii at Manoa. Stewart W. Wilson is a scientist at The Rowland Institute for Science, Cambridge, Massachusetts. Topics covered: The Animat Approach to Adaptive Behavior. Perception and Motor Control. Action Selection and Behavioral Sequences. Cognitive Maps and Internal World Models. Learning. Evolution. Collective Behavior.
    Note: "A Bradford book."
    Language: English
    URL: MIT CogNet
    URL: OCLC metadata license agreement
    URL: lizenzpflichtig
    URL: lizenzpflichtig
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    HU Berlin
    FU Berlin
    MPI Bildungsforschung
  • 2
    Online Resource
    Online Resource
    Learning Classifier Systems : 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002. Revised Papers (2003)
    Berlin, Heidelberg :Springer Berlin Heidelberg,
    Details
    UID:
    almahu_9947920453402882
    Format: VII, 233 p. , online resource.
    ISBN: 9783540400295
    Series Statement: Lecture Notes in Computer Science, 2661
    Content: The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7–8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi?er systems, focusing on the multi-agent problem known as El-farol. Holmes and Bilker investigate the effect that various types of missing data have on the classi?cation performance of learning classi?er systems. The two papers by Kovacs deal with an important theoretical issue in learning classi?er systems: the use of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve.
    Note: Balancing Specificity and Generality in a Panmictic-Based Rule-Discovery Learning Classifier System -- A Ruleset Reduction Algorithm for the XCS Learning Classifier System -- Adapted Pittsburgh-Style Classifier-System: Case-Study -- The Effect of Missing Data on Learning Classifier System Learning Rate and Classification Performance -- XCS’s Strength-Based Twin: Part I -- XCS’s Strength-Based Twin: Part II -- Further Comparison between ATNoSFERES and XCSM -- Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection -- Anticipatory Classifier System Using Behavioral Sequences in Non-Markov Environments -- Mapping Artificial Immune Systems into Learning Classifier Systems -- The 2003 Learning Classifier Systems Bibliography.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540205449
    Language: English
    Subjects: Computer Science
    RVK:
    SS 4800
    Keywords: Konferenzschrift
    DOI: 10.1007/b94229
    URL: http://dx.doi.org/10.1007/b94229
    URL: lizenzpflichtig
    URL: Volltext  (lizenzpflichtig)
    URL: Inhaltsverzeichnis
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  • 3
    Online Resource
    Online Resource
    Advances in Learning Classifier Systems : Third International Workshop, IWLCS 2000 Paris, France, September 15–16, 2000 Revised Papers (2001)
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    Details
    UID:
    gbv_1649256841
    Format: Online-Ressource
    ISBN: 9783540446408
    Series Statement: Lecture Notes in Computer Science 1996
    Content: This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Learning Classifier Systems, IWLCS 2000, held in Paris, France in September 2000. The 13 revised full papers presented have gone through two rounds of reviewing and selection. Also included is a comprehensive LCS bibliography listing more than 600 entries as well as an appendix. The papers are organized in topical sections on theory, applications, and advanced architectures
    Additional Edition: ISBN 9783540424376
    Additional Edition: Buchausg. u.d.T. Advances in learning classifier systems Berlin : Springer, 2001 ISBN 3540424377
    Language: English
    Subjects: Computer Science
    RVK:
    SS 4800
    Keywords: Lernendes System ; Automatische Klassifikation ; Lernendes System ; Produktionsregelsystem ; Lernendes System ; Genetischer Algorithmus ; Lernendes System ; Automatische Klassifikation ; Wissensextraktion ; Konferenzschrift
    DOI: 10.1007/3-540-44640-0
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
    URL: Inhaltsverzeichnis
    URL: Kapitel 1
    URL: Volltext
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  • 4
    Online Resource
    Online Resource
    Advances in Learning Classifier Systems : Third International Workshop, IWLCS 2000, Paris, France, September 15-16, 2000. Revised Papers (2001)
    Berlin, Heidelberg :Springer Berlin Heidelberg :
    Details
    UID:
    almahu_9948621575302882
    Format: VIII, 280 p. , online resource.
    Edition: 1st ed. 2001.
    ISBN: 9783540446408
    Series Statement: Lecture Notes in Artificial Intelligence ; 1996
    Content: Learning 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.
    Note: Theory -- 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.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783662169148
    Additional Edition: Printed edition: ISBN 9783540424376
    Language: English
    DOI: 10.1007/3-540-44640-0
    URL: https://doi.org/10.1007/3-540-44640-0
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    HU Berlin
  • 5
    Online Resource
    Online Resource
    Advances in Learning Classifier Systems : 4th International Workshop, IWLCS 2001 San Francisco, CA, USA, July 7–8, 2001 Revised Papers (2006)
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    Details
    UID:
    gbv_1646451015
    Format: Online-Ressource , v.: digital
    ISBN: 9783540481041
    Series Statement: Lecture Notes in Computer Science 2321
    Content: Theory -- Biasing Exploration in an Anticipatory Learning Classifier System -- An Incremental Multiplexer Problem and Its Uses in Classifier System Research -- A Minimal Model of Communication for a Multi-agent Classifier System -- A Representation for Accuracy-Based Assessment of Classifier System Prediction Performance -- A Self-Adaptive XCS -- Two Views of Classifier Systems -- Social Simulation Using a Multi-agent Model Based on Classifier Systems: The Emergence of Vacillating Behaviour in the “El Farol” Bar Problem -- Applications -- XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining -- A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool -- Explorations in LCS Models of Stock Trading -- On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems -- Compact Rulesets from XCSI -- An Algorithmic Description of ACS2.
    Note: In: Springer-Online
    Additional Edition: ISBN 9783540437932
    Additional Edition: Buchausg. u.d.T. Advances in learning classifier systems Berlin : Springer, 2002 ISBN 3540437932
    Language: English
    Subjects: Computer Science
    RVK:
    SS 4800
    Keywords: Lernendes System ; Automatische Klassifikation ; Konferenzschrift
    DOI: 10.1007/3-540-48104-4
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
    URL: Inhaltsverzeichnis
    URL: Kapitel 1
    URL: Inhaltstext
    Author information: Lanzi, Pier Luca 1967-
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  • 6
    Online Resource
    Online Resource
    Learning Classifier Systems : From Foundations to Applications (2000)
    Berlin, Heidelberg :Springer Berlin Heidelberg :
    Details
    UID:
    almahu_9948621678402882
    Format: X, 354 p. , online resource.
    Edition: 1st ed. 2000.
    ISBN: 9783540450276
    Series Statement: Lecture Notes in Artificial Intelligence ; 1813
    Content: Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.
    Note: Basics -- What Is a Learning Classifier System? -- A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999) -- State of XCS Classifier System Research -- An Introduction to Learning Fuzzy Classifier Systems -- Advanced Topics -- Fuzzy and Crisp Representations of Real-Valued Input for Learning Classifier Systems -- Do We Really Need to Estimate Rule Utilities in Classifier Systems? -- Strength or Accuracy? Fitness Calculation in Learning Classifier Systems -- Non-homogeneous Classifier Systems in a Macro-evolution Process -- An Introduction to Anticipatory Classifier Systems -- A Corporate XCS -- Get Real! XCS with Continuous-Valued Inputs -- Applications -- XCS and the Monk's Problems -- Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases -- An Adaptive Agent Based Economic Model -- The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques -- Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems -- The Bibliography -- A Learning Classifier Systems Bibliography.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783662172186
    Additional Edition: Printed edition: ISBN 9783540677291
    Language: English
    DOI: 10.1007/3-540-45027-0
    URL: https://doi.org/10.1007/3-540-45027-0
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
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    HU Berlin
  • 7
    Online Resource
    Online Resource
    Advances in Learning Classifier Systems : Third International Workshop, IWLCS 2000 Paris, France, September 15–16, 2000 Revised Papers (2001)
    Berlin, Heidelberg :Springer Berlin Heidelberg,
    Details
    UID:
    almahu_9947920570302882
    Format: VIII, 280 p. , online resource.
    ISBN: 9783540446408
    Series Statement: Lecture Notes in Computer Science, 1996
    Content: Learning 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.
    Note: Theory -- 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.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540424376
    Language: English
    DOI: 10.1007/3-540-44640-0
    URL: http://dx.doi.org/10.1007/3-540-44640-0
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Open Access Request
    HU Berlin
  • 8
    Online Resource
    Online Resource
    Learning Classifier Systems : 5th International Workshop, IWLCS 2002, Granada, Spain, September 7-8, 2002, Revised Papers (2003)
    Berlin, Heidelberg :Springer Berlin Heidelberg :
    Details
    UID:
    almahu_9948621549902882
    Format: VII, 233 p. , online resource.
    Edition: 1st ed. 2003.
    ISBN: 9783540400295
    Series Statement: Lecture Notes in Artificial Intelligence ; 2661
    Content: The 5th International Workshop on Learning Classi?er Systems (IWLCS2002) was held September 7-8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the ?rst paper, Browne introduces a new model of learning classi?er system, iLCS, and tests it on the Wisconsin Breast Cancer classi?cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi?er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi?er systems, focusing on the multi-agent problem known as El-farol. Holmes and Bilker investigate the effect that various types of missing data have on the classi?cation performance of learning classi?er systems. The two papers by Kovacs deal with an important theoretical issue in learning classi?er systems: the use of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve.
    Note: Balancing Specificity and Generality in a Panmictic-Based Rule-Discovery Learning Classifier System -- A Ruleset Reduction Algorithm for the XCS Learning Classifier System -- Adapted Pittsburgh-Style Classifier-System: Case-Study -- The Effect of Missing Data on Learning Classifier System Learning Rate and Classification Performance -- XCS's Strength-Based Twin: Part I -- XCS's Strength-Based Twin: Part II -- Further Comparison between ATNoSFERES and XCSM -- Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection -- Anticipatory Classifier System Using Behavioral Sequences in Non-Markov Environments -- Mapping Artificial Immune Systems into Learning Classifier Systems -- The 2003 Learning Classifier Systems Bibliography.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783540205449
    Additional Edition: Printed edition: ISBN 9783662172568
    Language: English
    DOI: 10.1007/b94229
    URL: https://doi.org/10.1007/b94229
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    Open Access Request
    HU Berlin
  • 9
    Online Resource
    Online Resource
    Advances in Learning Classifier Systems : 4th International Workshop, IWLCS 2001 San Francisco, CA, USA, July 7–8, 2001 Revised Papers (2002)
    Berlin, Heidelberg :Springer Berlin Heidelberg,
    Details
    UID:
    almahu_9947920444102882
    Format: VIII, 236 p. , online resource.
    ISBN: 9783540481041
    Series Statement: Lecture Notes in Computer Science, 2321
    Note: Theory -- Biasing Exploration in an Anticipatory Learning Classifier System -- An Incremental Multiplexer Problem and Its Uses in Classifier System Research -- A Minimal Model of Communication for a Multi-agent Classifier System -- A Representation for Accuracy-Based Assessment of Classifier System Prediction Performance -- A Self-Adaptive XCS -- Two Views of Classifier Systems -- Social Simulation Using a Multi-agent Model Based on Classifier Systems: The Emergence of Vacillating Behaviour in the “El Farol” Bar Problem -- Applications -- XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining -- A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool -- Explorations in LCS Models of Stock Trading -- On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems -- Compact Rulesets from XCSI -- An Algorithmic Description of ACS2.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540437932
    Language: English
    DOI: 10.1007/3-540-48104-4
    URL: http://dx.doi.org/10.1007/3-540-48104-4
    Library Location Call Number Volume/Issue/Year Availability
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    HU Berlin
  • 10
    Online Resource
    Online Resource
    Learning Classifier Systems : From Foundations to Applications (2000)
    Berlin, Heidelberg :Springer Berlin Heidelberg,
    Details
    UID:
    almahu_9947920531202882
    Format: X, 354 p. , online resource.
    ISBN: 9783540450276
    Series Statement: Lecture Notes in Computer Science, 1813
    Content: Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.
    Note: Basics -- What Is a Learning Classifier System? -- A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999) -- State of XCS Classifier System Research -- An Introduction to Learning Fuzzy Classifier Systems -- Advanced Topics -- Fuzzy and Crisp Representations of Real-Valued Input for Learning Classifier Systems -- Do We Really Need to Estimate Rule Utilities in Classifier Systems? -- Strength or Accuracy? Fitness Calculation in Learning Classifier Systems -- Non-homogeneous Classifier Systems in a Macro-evolution Process -- An Introduction to Anticipatory Classifier Systems -- A Corporate XCS -- Get Real! XCS with Continuous-Valued Inputs -- Applications -- XCS and the Monk’s Problems -- Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases -- An Adaptive Agent Based Economic Model -- The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques -- Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems -- The Bibliography -- A Learning Classifier Systems Bibliography.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540677291
    Language: English
    DOI: 10.1007/3-540-45027-0
    URL: http://dx.doi.org/10.1007/3-540-45027-0
    Library Location Call Number Volume/Issue/Year Availability
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