IDEAS home Printed from https://ideas.repec.org/a/tdt/annals/vxviiiy2012p185-188.html
   My bibliography  Save this article

Multiagent Learning within a collaborative environment

Author

Listed:
  • Cristina Ofelia STANCIU
  • Adrian COJOCARIU

    (”TIBISCUS” UNIVERSITY OF TIMIŞOARA)

  • Ljubica KAZI

    (UNIVERSITY OF NOVI SAD)

Abstract

Multiagent Learning is at the intersection of multiagent systems and Machine Learning, two subdomains of artificial intelligence. Traditional Machine Learning technologies usually imply a single agent that is trying to maximize some utility functions without having any knowledge about other agents within its environment. The multiagent systems domain refers to the domains where several agents are involved and mechanisms for the independent agents’ behaviors interaction have to be considered. Due to multiagent systems’ complexity, there have to be found solutions for using Machine Learning technologies to manage this complexity.

Suggested Citation

  • Cristina Ofelia STANCIU & Adrian COJOCARIU & Ljubica KAZI, 2012. "Multiagent Learning within a collaborative environment," Anale. Seria Stiinte Economice. Timisoara, Faculty of Economics, Tibiscus University in Timisoara, vol. 0, pages 185-188, May.
  • Handle: RePEc:tdt:annals:v:xviii:y:2012:p:185-188
    as

    Download full text from publisher

    File URL: http://fse.tibiscus.ro/anale/Lucrari2012/kssue2012_026.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Machine Learning; Multiagent Learning; Multiagent Systems;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tdt:annals:v:xviii:y:2012:p:185-188. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ramona Violeta Vasilescu (email available below). General contact details of provider: https://edirc.repec.org/data/fettiro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.