An intelligent agent for bilateral negotiation with unknown opponents in continuous-time domains

Automated negotiation among self-interested autonomous agents has gained tremendous attention due to the diversity of its broad range of potential real-world applications. This article deals with a prominent type of such negotiations, namely, multiissue negotiation that runs under continuous-time constraints and in which the negotiating agents have no prior knowledge about their opponents' preferences and strategies. A negotiation strategy called Dragon is described that employs sparse pseudoinput Gaussian processes. Specifically, Dragon enables an agent (1) to precisely model the behavior of its opponents with comparably low computational load and (2) to make decisions effectively and adaptively in very complex negotiation settings. Extensive experimental results, based on a number of negotiation scenarios and state-of-the-art negotiating agents from Automated Negotiating Agents Competitions, are provided. Moreover, the robustness of our strategy is evaluated through both empirical game-theoretic and spatial evolutionary game-theoretic analysis.