Skip to main content
placeholder image

The prediction of partners' behaviors in self-interested agents

Chapter


Abstract


  • Prediction partners' behaviors in negotiation has been an active research direction

    in recent years. By employing the estimation results, agents can modify their negotiation

    strategies in order to achieve an agreement much quicker or to gain higher benefits. Some

    of estimation strategies have been proposed by researchers, and most of them are based on

    machine learning mechanisms. However the machine learning based approach may not work

    well in some open and dynamic domains for the reasons of (1) lacking of sufficient data to

    train the system, and (2) requesting plenty of resources in each training process. Furthermore,

    because the estimation results may have errors, so single result maybe not accurate and practical

    enough in most situations. In order to address these issues mentioned above, we propose

    a quadratic regression analysis approach to predict partners' behaviors in this paper. The proposed

    approach is based only on the history of the offers during the current negotiation and

    does not require any training process in advance. This approach can estimate an interval of

    behaviors according to an accuracy requirement. The experimental results illustrate that by

    employing the proposed mechanism, agents can gain more accurate estimation results on partners'

    behaviors by comparing with other two estimation functions.

Publication Date


  • 2008

Citation


  • Ren, F. & Zhang, M. (2008). The prediction of partners' behaviors in self-interested agents. In M. Yokoo, T. Ito, M. Zhang, J. Lee & T. Matsuo (Eds.), Electronic Commerce: Theory and Practice (pp. 157-170). United States: Springer.

International Standard Book Number (isbn) 13


  • 9783540778080

Scopus Eid


  • 2-s2.0-44649171534

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2417

Book Title


  • Electronic Commerce: Theory and Practice

Start Page


  • 157

End Page


  • 170

Place Of Publication


  • United States

Abstract


  • Prediction partners' behaviors in negotiation has been an active research direction

    in recent years. By employing the estimation results, agents can modify their negotiation

    strategies in order to achieve an agreement much quicker or to gain higher benefits. Some

    of estimation strategies have been proposed by researchers, and most of them are based on

    machine learning mechanisms. However the machine learning based approach may not work

    well in some open and dynamic domains for the reasons of (1) lacking of sufficient data to

    train the system, and (2) requesting plenty of resources in each training process. Furthermore,

    because the estimation results may have errors, so single result maybe not accurate and practical

    enough in most situations. In order to address these issues mentioned above, we propose

    a quadratic regression analysis approach to predict partners' behaviors in this paper. The proposed

    approach is based only on the history of the offers during the current negotiation and

    does not require any training process in advance. This approach can estimate an interval of

    behaviors according to an accuracy requirement. The experimental results illustrate that by

    employing the proposed mechanism, agents can gain more accurate estimation results on partners'

    behaviors by comparing with other two estimation functions.

Publication Date


  • 2008

Citation


  • Ren, F. & Zhang, M. (2008). The prediction of partners' behaviors in self-interested agents. In M. Yokoo, T. Ito, M. Zhang, J. Lee & T. Matsuo (Eds.), Electronic Commerce: Theory and Practice (pp. 157-170). United States: Springer.

International Standard Book Number (isbn) 13


  • 9783540778080

Scopus Eid


  • 2-s2.0-44649171534

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/2417

Book Title


  • Electronic Commerce: Theory and Practice

Start Page


  • 157

End Page


  • 170

Place Of Publication


  • United States