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Expectation of trading agent behaviour in negotiation of electronic marketplace

Journal Article


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Abstract


  • Electronic Commerce has been a very significant commercial phenomenon in recent years, and autonomous agents are

    widely adopted by business or individuals in electronic marketplaces to fulfill time consuming tasks in trading. Agent negotiation

    mechanisms are usually applied between conflicted agents in order to reach a mutually beneficial agreement. Prediction of

    trading agents’ strategies and behaviours in negotiation is a very significant research topic in agent negotiation. By employing

    the prediction results on opponents’ possible strategies and behaviours during a negotiation, trading agents can plan and perform

    corresponding strategies in order to maximize their own profits. Significant achievements have been made on this topic. However,

    most existing approaches are based on machine learning mechanisms, which may fail to capture opponents’ behaviours in open

    and dynamic electronic marketplaces. In this paper, two agent behaviour expectation approaches are introduced to help trading

    agents to capture opponents’ potential behaviours during a negotiation in complex e-marketplaces. (i) The regression analysis

    approach focuses on illustrating the main trends of opponents’ trading behaviours; (ii) the vector analysis approach pays more

    attention to identifying opponents’ detailed negotiation strategies. The experimental results show the efficiency and efficacy of

    the two proposed approaches in open and dynamic negotiation environments.

Publication Date


  • 2012

Citation


  • Ren, F., Zhang, M. & Fulcher, J. (2012). Expectation of trading agent behaviour in negotiation of electronic marketplace. Web Intelligence and Agent Systems: an international journal, 10 (1), 49-63.

Scopus Eid


  • 2-s2.0-84863343787

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1031&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/27

Has Global Citation Frequency


Number Of Pages


  • 14

Start Page


  • 49

End Page


  • 63

Volume


  • 10

Issue


  • 1

Place Of Publication


  • Netherlands

Abstract


  • Electronic Commerce has been a very significant commercial phenomenon in recent years, and autonomous agents are

    widely adopted by business or individuals in electronic marketplaces to fulfill time consuming tasks in trading. Agent negotiation

    mechanisms are usually applied between conflicted agents in order to reach a mutually beneficial agreement. Prediction of

    trading agents’ strategies and behaviours in negotiation is a very significant research topic in agent negotiation. By employing

    the prediction results on opponents’ possible strategies and behaviours during a negotiation, trading agents can plan and perform

    corresponding strategies in order to maximize their own profits. Significant achievements have been made on this topic. However,

    most existing approaches are based on machine learning mechanisms, which may fail to capture opponents’ behaviours in open

    and dynamic electronic marketplaces. In this paper, two agent behaviour expectation approaches are introduced to help trading

    agents to capture opponents’ potential behaviours during a negotiation in complex e-marketplaces. (i) The regression analysis

    approach focuses on illustrating the main trends of opponents’ trading behaviours; (ii) the vector analysis approach pays more

    attention to identifying opponents’ detailed negotiation strategies. The experimental results show the efficiency and efficacy of

    the two proposed approaches in open and dynamic negotiation environments.

Publication Date


  • 2012

Citation


  • Ren, F., Zhang, M. & Fulcher, J. (2012). Expectation of trading agent behaviour in negotiation of electronic marketplace. Web Intelligence and Agent Systems: an international journal, 10 (1), 49-63.

Scopus Eid


  • 2-s2.0-84863343787

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1031&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/27

Has Global Citation Frequency


Number Of Pages


  • 14

Start Page


  • 49

End Page


  • 63

Volume


  • 10

Issue


  • 1

Place Of Publication


  • Netherlands