Abstract
In many situations, a form of negotiation can be used to resolve a problem between multiple parties. However, one of the biggest problems is not knowing the intentions and true interests of the opponent. Such a user profile can be learned or estimated using biddings as evidence that reveal some of the underlying interests. In this paper we present a model for online learning of an opponent model in a closed bilateral negotiation session. We studied the obtained utility during several negotiation sessions. Results show a significant improvement in utility when the agent negotiates against a state-of-the-art Bayesian agent, but also that results are very domain-dependent.
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van Galen Last, N. (2012). Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds) New Trends in Agent-Based Complex Automated Negotiations. Studies in Computational Intelligence, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24696-8_12
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DOI: https://doi.org/10.1007/978-3-642-24696-8_12
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