Advances in Robot Learning: 8th European Workhop on Learning Robots, EWLR-8 Lausanne, Switzerland, September 18, 1999 Proceedings: 1812 (Lecture Notes in Artificial Intelligence) - Softcover

9783540411628: Advances in Robot Learning: 8th European Workhop on Learning Robots, EWLR-8 Lausanne, Switzerland, September 18, 1999 Proceedings: 1812 (Lecture Notes in Artificial Intelligence)

Softcover

ISBN 10: 3540411623 ISBN 13: 9783540411628

Verlag: Springer, 2000

This book constitutes the thoroughly refereed post-workshop proceedings of the 8th European Workshop on Learning Robots, EWLR'99, held in Lausanne, Switzerland in September 1999. The seven revised full workshop papers presented were carefully reviewed and selected for inclusion in the book. Also included are two invited full papers. Among the topics addressed are map building for robot navigation, multi-task reinforcement learning, neural network approaches, example-based learning, situated agents, planning maps for mobile robots, path finding, autonomous robots, and biologically inspired approaches.

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Rese�a del editor

Robot learning is an exciting and interdisciplinary ?eld. This state is re?ected in the range and form of the papers presented here. Techniques that have - come well established in robot learning are present: evolutionary methods, neural networkapproaches, reinforcement learning; as are techniques from control t- ory, logic programming, and Bayesian statistics. It is notalbe that in many of the papers presented in this volume several of these techniques are employed in conjunction. In papers by Nehmzow, Grossmann and Quoy neural networks are utilised to provide landmark-based representations of the environment, but di?erent techniques are used in each paper to make inferences based on these representations. Biology continues to provide inspiration for the robot learning researcher. In their paper Peter Eggenberger et al. borrow ideas about the role of n- romodulators in switching neural circuits, These are combined with standard techniques from arti?cial neural networks and evolutionary computing to p- vide a powerful new algorithm for evolving robot controllers. In the ?nal paper in this volume Bianco and Cassinis combine observations about the navigation behaviour of insects with techniques from control theory to produce their visual landmarklearning system. Hopefully this convergence of engineering and biol- ical approaches will continue. A rigourous understanding of the ways techniques from these very di?erent disciplines can be fused is an important challenge if progress is to continue. Al these papers are also testament to the utility of using robots to study intelligence and adaptive behaviour.

Rese�a del editor

This book constitutes the thoroughly refereed post-workshop proceedings of the 8th European Workshop on Learning Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.
The seven revised full workshop papers presented were carefully reviewed and selected for inclusion in the book. Also included are two invited full papers. Among the topics addressed are map building for robot navigation, multi-task reinforcement learning, neural network approaches, example-based learning, situated agents, planning maps for mobile robots, path finding, autonomous robots, and biologically inspired approaches.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.

VerlagSpringer
Erscheinungsdatum2000
ISBN 10 3540411623
ISBN 13 9783540411628
EinbandTapa blanda
SpracheEnglisch
Anzahl der Seiten180
HerausgeberWyatt Jeremy
Kontakt zum HerstellerNicht verf�gbar

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Advances in Robot Learning

Wyatt, Jeremy|Demiris, John

Verlag: Springer Berlin Heidelberg, 2000

ISBN 10: 3540411623 ISBN 13: 9783540411628

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Advances in Robot Learning : 8th European Workhop on Learning Robots, EWLR-8 Lausanne, Switzerland, September 18, 1999 Proceedings

John Demiris

Verlag: Springer Berlin Heidelberg, 2000

ISBN 10: 3540411623 ISBN 13: 9783540411628

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Robot learning is an exciting and interdisciplinary eld. This state is re ected in the range and form of the papers presented here. Techniques that have - come well established in robot learning are present: evolutionary methods, neural networkapproaches, reinforcement learning; as are techniques from control t- ory, logic programming, and Bayesian statistics. It is notalbe that in many of the papers presented in this volume several of these techniques are employed in conjunction. In papers by Nehmzow, Grossmann and Quoy neural networks are utilised to provide landmark-based representations of the environment, but di erent techniques are used in each paper to make inferences based on these representations. Biology continues to provide inspiration for the robot learning researcher. In their paper Peter Eggenberger et al. borrow ideas about the role of n- romodulators in switching neural circuits, These are combined with standard techniques from arti cial neural networks and evolutionary computing to p- vide a powerful new algorithm for evolving robot controllers. In the nal paper in this volume Bianco and Cassinis combine observations about the navigation behaviour of insects with techniques from control theory to produce their visual landmarklearning system. Hopefully this convergence of engineering and biol- ical approaches will continue. A rigourous understanding of the ways techniques from these very di erent disciplines can be fused is an important challenge if progress is to continue. Al these papers are also testament to the utility of using robots to study intelligence and adaptive behaviour. Artikel-Nr. 9783540411628

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