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Cybernetics and Learning Automata

Chapter
Part of the Springer Handbooks book series (SHB)

Abstract

Stochastic learning automata are probabilistic finite state machines which have been used to model how biological systems can learn. The structure of such a machine can be fixed or can be changing with time. A learning automaton can also be implemented using action (choosing) probability updating rules which may or may not depend on estimates from the environment being investigated. This chapter presents an overview of the field of learning automata, perceived as a completely new paradigm for learning, and explains how it is related to the area of cybernetics.

Abbreviations

ATM

air traffic management

ATM

asynchronous transfer mode

ATM

automatic teller machine

DEA

discrete estimator algorithm

DGPA

discretized generalized pursuit algorithm

DPA

discrete pursuit algorithm

DTSE

discrete TSE algorithm

FSSA

fixed structure stochastic automaton

GPA

generalized pursuit algorithm

IP

inaction–penalty

IP

industrial protocol

IP

integer programming

IP

intellectual property

IP

internet protocol

LA

learning automata

RE

random environment

RI

reward–inaction

RNG

random-number generator

RP

reward–penalty

SELA

stochastic estimator learning algorithm

TSE

total system error

VSSA

variable structure stochastic automata

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.School of Computer ScienceCarleton UniversityOttawaCanada
  2. 2.School of Information TechnologyIndian Institute of TechnologyKharagpurIndia

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