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Author Fagg, A.H. ♦ Lotspeich, D. ♦ Bekey, G.A.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1994
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Other branches of engineering
Subject Keyword Control systems ♦ Intelligent robots ♦ Robot sensing systems ♦ Service robots ♦ Robot control ♦ Intelligent systems ♦ Computational and artificial intelligence ♦ Optimal control ♦ Application specific integrated circuits ♦ Data mining
Abstract Within the field of robotics, much recent attention has been given to control techniques that have been termed reactive or behavior-based. The design of such control systems for even a remotely interesting task is typically a laborious effort, requiring many hours of experimental "tweaking" as the actual behavior of the system is observed by the system designer. In this paper, the authors present a neural-based reinforcement learning approach to the design of reactive control policies in which the designer specifies the the desired behavior of the system, rather than the control program that produces the desired behavior.
Description Author affiliation: Center for Neural Eng., Univ. of Southern California, Los Angeles, CA, USA (Fagg, A.H.; Lotspeich, D.; Bekey, G.A.)
ISBN 0818653302
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1994-05-08
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 735.38 kB
Page Count 6
Starting Page 39
Ending Page 44


Source: IEEE Xplore Digital Library