Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation


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.

New Trends in Agent-Based Complex Automated Negotiations
Niels van Galen Last
Full Stack Data Scientist

My research interests include distributed robotics, mobile computing and programmable matter. Foo, bar.