Anticipatory Learning Classifier Systems (Paperback, Softcover reprint of the original 1st ed. 2002)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Genetic Algorithms and Evolutionary Computation, 4
Release date:	March 2013
First published:	2002
Authors:	Martin V. Butz
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Paperback
Pages:	172
Edition:	Softcover reprint of the original 1st ed. 2002
ISBN-13:	978-1-4613-5290-7
Categories:	Books > Computing & IT > General theory of computing > General Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	1-4613-5290-8
Barcode:	9781461352907

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