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Anticipatory Learning Classifier Systems (Genetic Algorithms and Evolutionary Computation) [Hardcover]

Martin V. Butz (Author)

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Book Description

Publication Date: January 25, 2002 | ISBN-10: 0792376307 | ISBN-13: 978-0792376309 | Edition: 1

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.

#outer_postBodyPS { display: none; } #psGradient { display: none; } #psPlaceHolder { display: none; } #psExpand { display: none; } 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.

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Product Details

Hardcover: 200 pages
Publisher: Springer; 1 edition (January 25, 2002)
Language: English
ISBN-10: 0792376307
ISBN-13: 978-0792376309
Product Dimensions: 9.3 x 6.1 x 0.7 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Amazon Best Sellers Rank: #4,533,538 in Books (See Top 100 in Books)

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Inside This Book (learn more)

First Sentence:
In order not to precipitately rush into the theoretical details and functioning of ACS2, we first provide a rather general background of anticipations, genetic algorithms, and learning classifier systems. Read the first page

Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
knowledge array bias, genetic generalization mechanism, generalizing mutation, anticipatory learning process, evolving environmental model, model aliasing problem, perceptual string, immediate reward prediction, compact environmental model, anticipatory learning classifier systems, model learning speed, overlapping classifiers, generalized environmental model, anticipatory classifier system, genetic generalization pressure, accuracy based fitness approach, offspring classifier, reward prediction values, reinforcement learning capabilities, anticipatory behavioral control, exploration bias, gripper problem, acting algorithm, action set size, motivational module

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Citations (learn more)

This book cites 17 books:

Learning Classifier Systems: From Foundations to Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence) by Pier L. Lanzi in Back Matter (1), Back Matter (2), and Back Matter (3)
Genetic Algorithms [VHS] by KOZA in Back Matter (1), and Back Matter (2)
Advances in Learning Classifier Systems by Pier L. Lanzi in Back Matter (1), and Back Matter (2)
Reinforcement Learning (The Springer International Series in Engineering and Computer Science) by Richard S. Sutton in Front Matter, and page 29
From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior (Complex Adaptive Systems) by Jean-Arcady Meyer in Back Matter (1), and Back Matter (2)

See all 17 books this book cites

7 books cite this book:

OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems (Genetic Algorithms and Evolutionary Computation, Volume 6) by Dimitri Knjazew in Front Matter
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing) by Ajith Abraham on page 251
Evolutionary Optimization in Dynamic Environments (Genetic Algorithms and Evolutionary Computation) by J�rgen Branke in Front Matter
Frontiers of Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) by Anil Menon in Front Matter
Noisy Optimization with Evolution Strategies (Genetic Algorithms and Evolutionary Computation) by Dirk V. Arnold in Front Matter

See all 7 books citing this book

Books on Related Topics (learn more)

Rule-Based Evolutionary Online Learning Systems by Martin V. Butz

Discusses:

Advances in Learning Classifier Systems by Pier L. Lanzi

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Learning Classifier Systems by Pier Luca Lanzi

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Genetic and Evolutionary Computation--GECCO 2003 PART II by Erick Cantu-Paz

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