The Likeability-Success Tradeoff: Results of the 2nd Annual Human-Agent Automated Negotiating Agents Competition (bibtex)
by Mell, Johnathan, Gratch, Jonathan, Aydogan, Reyhan, Baarslag, Tim and Jonker, Catholijn M.
Abstract:
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 humanagent negotiation and provide a state-of-the-art benchmark for human-agent design.
Reference:
The Likeability-Success Tradeoff: Results of the 2nd Annual Human-Agent Automated Negotiating Agents Competition (Mell, Johnathan, Gratch, Jonathan, Aydogan, Reyhan, Baarslag, Tim and Jonker, Catholijn M.), In Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII), IEEE, 2019.
Bibtex Entry:
@inproceedings{mell_likeability-success_2019,
	address = {Cambridge, UK},
	title = {The {Likeability}-{Success} {Tradeoff}: {Results} of the 2nd {Annual} {Human}-{Agent} {Automated} {Negotiating} {Agents} {Competition}},
	url = {https://ieeexplore.ieee.org/xpl/conhome/8911251/proceeding},
	abstract = {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 humanagent negotiation and provide a state-of-the-art benchmark for human-agent design.},
	booktitle = {Proceedings of the 8th {International} {Conference} on {Affective} {Computing} and {Intelligent} {Interaction} ({ACII})},
	publisher = {IEEE},
	author = {Mell, Johnathan and Gratch, Jonathan and Aydogan, Reyhan and Baarslag, Tim and Jonker, Catholijn M.},
	month = sep,
	year = {2019},
	keywords = {Virtual Humans}
}
Powered by bibtexbrowser