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Anticipatory Learning Classifier Systems

2012-12-06 Computers
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Release : 2012-12-06
Genre : Computers
Kind : eBook
Book Rating : 916/5 ( reviews)

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Book Synopsis Anticipatory Learning Classifier Systems by : Martin V. Butz

Download or read book Anticipatory Learning Classifier Systems written by Martin V. Butz. This book was released on 2012-12-06. Available in PDF, EPUB and Kindle. Book excerpt: Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior. Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning. Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system.

Advances in Learning Classifier Systems

2003-07-31 Computers
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Release : 2003-07-31
Genre : Computers
Kind : eBook
Book Rating : 400/5 ( reviews)

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Book Synopsis Advances in Learning Classifier Systems by : Pier L. Lanzi

Download or read book Advances in Learning Classifier Systems written by Pier L. Lanzi. This book was released on 2003-07-31. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Learning Classifier Systems

2003-08-01 Computers
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Release : 2003-08-01
Genre : Computers
Kind : eBook
Book Rating : 044/5 ( reviews)

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Book Synopsis Advances in Learning Classifier Systems by : Pier L. Lanzi

Download or read book Advances in Learning Classifier Systems written by Pier L. Lanzi. This book was released on 2003-08-01. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Learning Classifier Systems, IWLCS 2001, held in San Francisco, CA, USA, in July 2001. The 12 revised full papers presented together with a special paper on a formal description of ACS have gone through two rounds of reviewing and improvement. The first part of the book is devoted to theoretical issues of learning classifier systems including the influence of exploration strategy, self-adaptive classifier systems, and the use of classifier systems for social simulation. The second part is devoted to applications in various fields such as data mining, stock trading, and power distributionn networks.

Anticipatory Behavior in Adaptive Learning Systems

2004-01-21 Computers
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Release : 2004-01-21
Genre : Computers
Kind : eBook
Book Rating : 025/5 ( reviews)

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Book Synopsis Anticipatory Behavior in Adaptive Learning Systems by : Martin V. Butz

Download or read book Anticipatory Behavior in Adaptive Learning Systems written by Martin V. Butz. This book was released on 2004-01-21. Available in PDF, EPUB and Kindle. Book excerpt: The interdisciplinary topic of anticipation, attracting attention fromnbsp;computer scientists, psychologists, philosophers, neuroscientists, and biologists is a rather new and often misunderstood matter of research. This book attempts to establish anticipation as a research topic and encourage further research and development work. First, the book presents philosophical thoughts and concepts to stimulate the reader's concern about the topic. Fundamental cognitive psychology experiments then confirm the existence of anticipatory behavior in animals and humans and outline a first framework of anticipatory learning and behavior. Next, several distinctions and frameworks of anticipatory processes are discussed, including first implementations of these concepts. Finally, several anticipatory systems and studies on anticipatory behavior are presented.

Learning Classifier Systems

2003-06-26 Computers
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Release : 2003-06-26
Genre : Computers
Kind : eBook
Book Rating : 270/5 ( reviews)

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Book Synopsis Learning Classifier Systems by : Pier L. Lanzi

Download or read book Learning Classifier Systems written by Pier L. Lanzi. This book was released on 2003-06-26. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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