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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