Optimizing Complex Automated Negotiation using Sparse Pseudo-Input Gaussian

Complex negotiations among rational autonomous agents is gain-ing a mass of attention due to the diversity of its possible appli-cations. This paper deals with a prominent type of complex ne-gotiations, namely, multi-issue negotiation that runs under real-time constraints and in which the negotiating agents have no prior knowledge about their opponents ’ preferences and strategies. 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. Specifically, with SPGPs Dragon is capable of: (1) efficiently modeling unknown opponents by means of a non-parametric functional prior; (2) significantly re-ducing the computational complexity of this functional prior; and (3) effectively and adaptively making decisions during negotiation. The experimental results provided in this paper show, both from the standard mean-score perspective and the perspective of empirical game theory, that Dragon outperforms the state-of-the-art negotia