Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments

ECAI'96 Workshop LDAIS Budapest, Hungary, August 13, 1996 ICMAS'96 Workshop LIOME Kyoto, Japan, December 10, 1996 Selected Papers

  • Editors
  • Gerhard Weiß

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1221)

Table of contents

  1. Front Matter
  2. Gerhard Weiß
    Pages 1-10
  3. Munindar P. Singh, Michael N. Huhns
    Pages 11-24
  4. Norihiko Ono, Kenji Fukumoto
    Pages 25-39
  5. Cristina Versino, Luca Maria Gambardella
    Pages 40-61
  6. Norihiko Ono, Yoshihiro Fukuta
    Pages 73-81
  7. Jürgen Schmidhuber, Jieyu Zhao
    Pages 82-93
  8. Yiming Ye, John K. Tsotsos
    Pages 94-116
  9. Masahiro Terabe, Takashi Washio, Osamu Katai, Tetsuo Sawaragi
    Pages 168-179
  10. Enric Plaza, Josep Lluís Arcos, Francisco Martín
    Pages 180-201
  11. Britta Lenzmann, Ipke Wachsmuth
    Pages 202-222
  12. Winton Davies, Peter Edwards
    Pages 223-241
  13. Holger Friedrich, Michael Kaiser, Oliver Rogalla, Rüdiger Dillmann
    Pages 259-275
  14. Nicholas Lacey, Keiichi Nakata, Mark Lee
    Pages 276-292
  15. Back Matter

About these proceedings


The complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information.


Kommunikation Kooperation Koordination Mehragenten-System Performance Verteilte künstliche Intelligenz algorithms case-based reasoning communication cooperation distributed artificial intelligence machine learning organization reinforcement learning robot

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-62934-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 1997
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-62934-4
  • Online ISBN 978-3-540-69050-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book