TY - GEN
T1 - The Likeability-Success Tradeoff
T2 - 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019
AU - Mell, Johnathan
AU - Gratch, Jonathan
AU - Aydogan, R.
AU - Baarslag, Tim
AU - Jonker, Catholijn M.
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2019
Y1 - 2019
N2 - We 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 human-agent negotiation and provide a state-of-the-art benchmark for human-agent design.
AB - We 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 human-agent negotiation and provide a state-of-the-art benchmark for human-agent design.
KW - Empirical results in HCI
KW - Human agent interaction
KW - Negotiation
UR - http://www.scopus.com/inward/record.url?scp=85077789815&partnerID=8YFLogxK
U2 - 10.1109/ACII.2019.8925437
DO - 10.1109/ACII.2019.8925437
M3 - Conference contribution
T3 - 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019
BT - 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019
PB - Institute of Electrical and Electronics Engineers (IEEE)
Y2 - 3 September 2019 through 6 September 2019
ER -