Y. Luo and L. Bölöni

Learning models of the negotiation partner in spatio-temporal collaboration


Cite as:

Y. Luo and L. Bölöni. Learning models of the negotiation partner in spatio-temporal collaboration. In The 4th International Conference on Collaborative Computing:Networking, Applications and Worksharing (CollaborateCom-2008), November 2008.

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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.

BibTeX:

@inproceedings{Luo-2008-CollaborateCom,
   author = "Y. Luo and L. B{\"o}l{\"o}ni",
   title = "Learning models of the negotiation partner in spatio-temporal collaboration",
   month = "November",
   year= "2008",
   booktitle="The 4th International Conference on Collaborative Computing:
Networking, Applications and Worksharing (CollaborateCom-2008)",
   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.
   },
}

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