Advances in Plan-Based Control of Robotic Agents

International Seminar Dagstuhl Castle, Germany, October 21–26, 2001 Revised Papers

  • Michael Beetz
  • Joachim Hertzberg
  • Malik Ghallab
  • Martha E. Pollack

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2466)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2466)

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Rachid Alami, Silvia Silva da Costa Bothelho
    Pages 1-20
  3. Michael Beetz, Andreas Hofhauser
    Pages 21-35
  4. Sebastian Buck, Thorsten Schmitt, Michael Beetz
    Pages 36-51
  5. Wolfram Burgard, Mark Moors, Frank Schneider
    Pages 52-70
  6. Hans-Dieter Burkhard, Joscha Bach, Ralf Berger, Birger Brunswieck, Michael Gollin
    Pages 71-88
  7. Silvia Coradeschi, Alessandro Saffiotti
    Pages 89-105
  8. Lars Karlsson, Tommaso Schiavinotto
    Pages 106-122
  9. Khaled Ben Lamine, Froduald Kabanza
    Pages 123-139
  10. Maxim Likhachev, Sven Koenig
    Pages 140-156
  11. Benoit Morisset, Malik Ghallab
    Pages 157-178
  12. Martha E. Pollack, Colleen E. McCarthy, Sailesh Ramakrishnan, Ioannis Tsamardinos
    Pages 179-192
  13. Dilip Kumar Pratihar, Wolfgang Bibel
    Pages 193-210
  14. Paul Rybski, Sascha Stoeter, Maria Gini, Dean Hougen, Nikolaos Papanikolopoulos
    Pages 211-225
  15. Frank Schönherr, Joachim Hertzberg
    Pages 249-269
  16. Shlomo Zilberstein, Richard Washington, Daniel S. Bernstein, Abdel-Illah Mouaddib
    Pages 270-289
  17. Back Matter
    Pages 291-291

About these proceedings

Introduction

In recent years, autonomous robots, including Xavier, Martha [1], Rhino [2,3], Minerva,and Remote Agent, have shown impressive performance in long-term demonstrations. In NASA’s Deep Space program, for example, an - tonomous spacecraft controller, called the Remote Agent [5], has autonomously performed a scienti?c experiment in space. At Carnegie Mellon University, Xavier [6], another autonomous mobile robot, navigated through an o?ce - vironment for more than a year, allowing people to issue navigation commands and monitor their execution via the Internet. In 1998, Minerva [7] acted for 13 days as a museum tourguide in the Smithsonian Museum, and led several thousand people through an exhibition. These autonomous robots have in common that they rely on plan-based c- trol in order to achieve better problem-solving competence. In the plan-based approach, robots generate control actions by maintaining and executing a plan that is e?ective and has a high expected utility with respect to the robots’ c- rent goals and beliefs. Plans are robot control programs that a robot can not only execute but also reason about and manipulate [4]. Thus, a plan-based c- troller is able to manage and adapt the robot’s intended course of action — the plan — while executing it and can thereby better achieve complex and changing tasks.

Keywords

agents algorithms artificial intelligence learning probabilistic reasoning problem solving robot robotics

Editors and affiliations

  • Michael Beetz
    • 1
  • Joachim Hertzberg
    • 2
  • Malik Ghallab
    • 3
  • Martha E. Pollack
    • 4
  1. 1.Institut für Informatik IXTechnische Universität MünchenMünchenGermany
  2. 2.Fraunhofer-Institut, Autonome Intelligente Systeme (AIS)Sankt AugustinGermany
  3. 3.LAAS-CNRSToulouse cedexFrance
  4. 4.College of Engineering Dept. of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborUSA

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-37724-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-00168-3
  • Online ISBN 978-3-540-37724-5
  • Series Print ISSN 0302-9743
  • About this book