Mell, J.Gratch, J.Aydoğan, ReyhanBaarslag, T.Jonker, C. M.2020-08-252020-08-252019978-1-7281-3888-62156-8103http://hdl.handle.net/10679/6823https://doi.org/10.1109/ACII.2019.8925437We present the results of the 2nd Annual Human-Agent League of the Automated Negotiating Agent Competition. Building on the success of the previous year's results, a new challenge was issued that focused exploring the likeability-success tradeoff in negotiations. By examining a series of repeated negotiations, actions may affect the relationship between automated negotiating agents and their human competitors over time. The results presented herein support a more complex view of human-agent negotiation and capture of integrative potential (win-win solutions). We show that, although likeability is generally seen as a tradeoff to winning, agents are able to remain well-liked while winning if integrative potential is not discovered in a given negotiation. The results indicate that the top-performing agent in this competition took advantage of this loophole by engaging in favor exchange across negotiations (cross-game logrolling). These exploratory results provide information about the effects of different submitted "black-box" agents in humanagent negotiation and provide a state-of-the-art benchmark for human-agent design.enginfo:eu-repo/semantics/restrictedAccessConference paper00052222080000310.1109/ACII.2019.8925437Human agent interactionNegotiationEmpirical results in HCI2-s2.0-85077789815