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Plan-Based Control of Robotic Agents: Improving the Capabilities of Autonomous Robots (Lecture Notes in Computer Science, 2554)

Plan-Based Control of Robotic Agents: Improving the Capabilities of Autonomous Robots (Lecture Notes in Computer Science, 2554)

Plan-Based Control of Robotic Agents: Improving the Capabilities of Autonomous
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Plan-Based Control of Robotic Agents: Improving the Capabilities of Autonomous Robots (Lecture Notes in Computer Science, 2554) Paperback - 2002 - 2002nd Edition

by Beetz, Michael

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Springer, 2002-12-13. 2002. paperback. New. 6.10x0.48x9.25. Buy with confidence. Excellent Customer Service & Return policy.
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  • Title Plan-Based Control of Robotic Agents: Improving the Capabilities of Autonomous Robots (Lecture Notes in Computer Science, 2554)
  • Author Beetz, Michael
  • Binding Paperback
  • Edition number 2002nd
  • Edition 2002
  • Condition New
  • Pages 194
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2002-12-13
  • Bookseller's Inventory # DADAX3540003355
  • ISBN 9783540003359 / 3540003355
  • Weight 0.66 lbs (0.30 kg)
  • Dimensions 9.21 x 6.14 x 0.44 in (23.39 x 15.60 x 1.12 cm)
  • Size 6.10x0.48x9.25
  • Category Computers - General Information
  • Library of Congress Catalogue Number 2002042892
  • Dewey Decimal Code 629.892
  • Quantity available 1

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Reader reviews for Plan-Based Control of Robotic Agents: Improving the Capabilities of Autonomous Robots (Lecture Notes in Computer Science, 2554)

From the publisher

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.

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