Skip to main content
SpringerLink
Log in

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

  • Conference proceedings
  • © 1997

Overview

Editors:
  1. Gerhard Weiß
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

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

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (18 papers)

  1. Front Matter

    Download chapter PDF

  2. Reader's guide

    • Gerhard Weiß
    Pages 1-10

  3. Challenges for machine learning in cooperative information systems

    • Munindar P. Singh, Michael N. Huhns
    Pages 11-24

  4. A modular approach to multi-agent reinforcement learning

    • Norihiko Ono, Kenji Fukumoto
    Pages 25-39

  5. Learning real team solutions

    • Cristina Versino, Luca Maria Gambardella
    Pages 40-61

  6. Learning by linear anticipation in multi-agent systems

    • Paul Davidsson
    Pages 62-72

  7. Learning coordinated behavior in a continuous environment

    • Norihiko Ono, Yoshihiro Fukuta
    Pages 73-81

  8. Multi-agent learning with the success-story algorithm

    • Jürgen Schmidhuber, Jieyu Zhao
    Pages 82-93

  9. On the collaborative object search team: a formulation

    • Yiming Ye, John K. Tsotsos
    Pages 94-116

  10. Evolution of coordination as a metaphor for learning in multi-agent systems

    • Ana L. C. Bazzan
    Pages 117-136

  11. Correlating internal parameters and external performance: Learning Soccer Agents

    • Rajani Nadella, Sandip Sen
    Pages 137-150

  12. Learning agents' reliability through Bayesian Conditioning: A simulation experiment

    • Aldo Franco Dragoni, Paolo Giorgini
    Pages 151-167

  13. A study of organizational learning in multiagents systems

    • Masahiro Terabe, Takashi Washio, Osamu Katai, Tetsuo Sawaragi
    Pages 168-179

  14. Cooperative Case-based Reasoning

    • Enric Plaza, Josep Lluís Arcos, Francisco Martín
    Pages 180-201

  15. Contract-net-based learning in a user-adaptive interface agency

    • Britta Lenzmann, Ipke Wachsmuth
    Pages 202-222

  16. The communication of inductive inferences

    • Winton Davies, Peter Edwards
    Pages 223-241

  17. Addressee Learning and Message Interception for communication load reduction in multiple robot environments

    • Takuya Ohko, Kazuo Hiraki, Yuichiro Anzai
    Pages 242-258

  18. Learning and communication in Multi-Agent Systems

    • Holger Friedrich, Michael Kaiser, Oliver Rogalla, Rüdiger Dillmann
    Pages 259-275

  19. Investigating the effects of explicit epistemology on a Distributed learning system

    • Nicholas Lacey, Keiichi Nakata, Mark Lee
    Pages 276-292

  20. Back Matter

    Download chapter PDF
Back to top

Other volumes

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

Keywords

About this book

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.

Bibliographic Information

  • Book Title: Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments

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

  • Editors: Gerhard Weiß

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/3-540-62934-3

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 1997

  • Softcover ISBN: 978-3-540-62934-4Published: 29 April 1997

  • eBook ISBN: 978-3-540-69050-4Published: 02 July 2005

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XII, 300

  • Topics: Artificial Intelligence, Simulation and Modeling, Programming Languages, Compilers, Interpreters

Publish with us

Policies and ethics

Back to top

Search

Navigation