Plan-Based Control of Robotic Agents : Improving the Capabilities of Autonomous Robots
Language: English
Published by Springer Vieweg, 2002
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 57.67
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - Robotic agents, such as autonomous office couriers or robot tourguides, must be both reliable and efficient. Thus, they have to flexibly interleave their tasks, exploit opportunities, quickly plan their course of action, and, if necessary, revise their intended activities.This book makes three major contributions to improving the capabilities of robotic agents:- first, a plan representation method is introduced which allows for specifying flexible andreliable behavior - second, probabilistic hybrid action models are presented as a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans - third, the system XFRMLEARN capable of learning structured symbolic navigation plans is described in detail.
Seller Inventory # 9783540003359
- Title
- Plan-Based Control of Robotic Agents : Improving the Capabilities of Autonomous Robots
- Author
- Michael Beetz
- Publisher
- Springer Vieweg
- Publication year
- 2002
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3540003355
- ISBN 13
- 9783540003359
- Item weight
- 324 grams
- Dimensions
- 235x155x12 mm
Robotic agents, such as autonomous office couriers or robot tourguides, must be both reliable and efficient. Thus, they have to flexibly interleave their tasks, exploit opportunities, quickly plan their course of action, and, if necessary, revise their intended activities.
This book makes three major contributions to improving the capabilities of robotic agents:
- first, a plan representation method is introduced which allows for specifying flexible and reliable behavior
- second, probabilistic hybrid action models are presented as a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans
- third, the system XFRMLEARN capable of learning structured symbolic navigation plans is described in detail.
"Synopsis" may belong to another edition of this title.
Product Description
"About the title" may belong to another edition of this title.
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