TY - GEN
T1 - Measuring the performance of online opponent models in automated bilateral negotiation
AU - Baarslag, Tim
AU - Hendrikx, Mark
AU - Hindriks, Koen
AU - Jonker, Catholijn
PY - 2012/12/26
Y1 - 2012/12/26
N2 - An important aim in bilateral negotiations is to achieve a win-win solution for both parties; therefore, a critical aspect of a negotiating agent's success is its ability to take the opponent's preferences into account. Every year, new negotiation agents are introduced with better learning techniques to model the opponent. Our main goal in this work is to evaluate and compare the performance of a selection of state-of-the-art online opponent modeling techniques in negotiation, and to determine under which circumstances they are beneficial in a real-time, online negotiation setting. Towards this end, we provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. This results in better insight into the performance of opponent models, and allows us to pinpoint well-performing opponent modeling techniques that did not receive much previous attention in literature.
AB - An important aim in bilateral negotiations is to achieve a win-win solution for both parties; therefore, a critical aspect of a negotiating agent's success is its ability to take the opponent's preferences into account. Every year, new negotiation agents are introduced with better learning techniques to model the opponent. Our main goal in this work is to evaluate and compare the performance of a selection of state-of-the-art online opponent modeling techniques in negotiation, and to determine under which circumstances they are beneficial in a real-time, online negotiation setting. Towards this end, we provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. This results in better insight into the performance of opponent models, and allows us to pinpoint well-performing opponent modeling techniques that did not receive much previous attention in literature.
KW - Negotiation
KW - Opponent Model Performance
KW - Quality Measures
UR - http://www.scopus.com/inward/record.url?scp=84871393245&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871393245&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35101-3_1
DO - 10.1007/978-3-642-35101-3_1
M3 - Conference contribution
AN - SCOPUS:84871393245
SN - 9783642351006
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 14
BT - AI 2012
T2 - 25th Australasian Joint Conference on Artificial Intelligence, AI 2012
Y2 - 4 December 2012 through 7 December 2012
ER -