Automated Negotiation Based on Sparse Pseudo-Input Gaussian Processes1

This paper deals with a prominent type of complex negotiations. We propose a novel negotiation strategy called Dragon which employs sparse pseudo-input Gaussian processes (SPGPs) to model efficiently the behavior of the negotiating opponents. The experimental results provided in this paper show that Dragon outperforms the state-of-the-art negotiation agents from the 2012 and 2011 Automated Negotiating Agents Competition (ANAC) in a variety of scenarios.