Collaborative Computing: Networking, Applications and Worksharing. 4th International Conference, CollaborateCom 2008, Orlando, FL, USA, November 13-16, 2008, Revised Selected Papers

Research Article

Learning Models of the Negotiation Partner in Spatio-temporal Collaboration

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  • @INPROCEEDINGS{10.1007/978-3-642-03354-4_18,
        author={Yi Luo and Ladislau B\o{}l\o{}ni},
        title={Learning Models of the Negotiation Partner in Spatio-temporal Collaboration},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 4th International Conference, CollaborateCom 2008, Orlando, FL, USA, November 13-16, 2008, Revised Selected Papers},
        proceedings_a={COLLABORATECOM},
        year={2012},
        month={5},
        keywords={},
        doi={10.1007/978-3-642-03354-4_18}
    }
    
  • Yi Luo
    Ladislau Bölöni
    Year: 2012
    Learning Models of the Negotiation Partner in Spatio-temporal Collaboration
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-642-03354-4_18
Yi Luo1,*, Ladislau Bölöni1,*
  • 1: University of Central Florida
*Contact email: yiluo@mail.ucf.edu, lboloni@eecs.ucf.edu

Abstract

We describe an approach for learning the model of the opponent in spatio-temporal negotiation. We use the Children in the Rectangular Forest canonical problem as an example. The opponent model is represented by the physical characteristics of the agents: the current location and the destination. We assume that the agents do not disclose any of their information voluntarily; the learning needs to rely on the study of the offers exchanged during normal negotiation. Our approach is Bayesian learning, with the main contribution being four techniques through which the posterior probabilities are determined. The calculations rely on (a) feasibility of offers, (b) rationality of offers, (c) the assumption of decreasing utility, and (d) the assumption of accepting offer which is better than the next counter-offer.