An Application of the INFINITE framework in a Human-Agent Negotiation Competition.

We discuss an application of the INFINITE negotiation architecture for developing agents that can negotiate with others while representing its user's preferences. We developed an agent, Draft Agent, that was entered into the 2019 Human-Agent league (HAL) of the Autonomous Negotiating Agent Competition (ANAC). We discuss Draft Agent's performance, highlighting where it worked well and aspects that can be further improved. A key feature of Draft Agent is the use of an alternate-issue-selection protocol to model the opponent's preference structure. The learnt preferences are then used to propose a fair, and where possible, win-win deal. Though this approach allows Draft Agent to obtain relatively high individual as well as joint utility, it might be considered somewhat rigid by human users and hence scores comparatively low on the likeability scale. We present a detailed analysis of the comparative performance of Draft Agent and the competing finalists of the HAL competition. We also suggest some options to further improve Draft Agent's performance and likeability.