Robot Learning (Hardcover, 1993 ed.)

Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.

General

Imprint:	Springer
Country of origin:	Netherlands
Series:	The Springer International Series in Engineering and Computer Science, 233
Release date:	June 1993
First published:	1993
Editors:	J. H. Connell • Sridhar Mahadevan
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Hardcover
Pages:	240
Edition:	1993 ed.
ISBN-13:	978-0-7923-9365-8
Categories:	Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > Robotics
LSN:	0-7923-9365-1
Barcode:	9780792393658

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