TY - GEN
T1 - An agent design for repeated negotiation and information revelation with people
AU - Peled, Noam
AU - Gal, Ya'akov
AU - Kraus, Sarit
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Many negotiations in the real world are characterized by incomplete information, and participants' success depends on their ability to reveal information in a way that facilitates agreement without compromising the individual gains of agents. This paper presents a novel agent design for repeated negotiation in incomplete information settings that learns to reveal information strategically during the negotiation process. The agent used classical machine learning techniques to predict how people make and respond to offers during the negotiation, how the y reveal information and their response to potential revelation actions by the agent. The agent was evaluated empirically in an extensive empirical study spanning hundreds of human subjects. Results show that the agent was able to outperform people. In particular, it learned (1) to make offers that were beneficial to people while not compromising its own benefit; (2) to incrementally reveal information to people in a way that increased its expected performance. The approach generalizes to new settings without the need to acquire additional data. This work demonstrates the efficacy of combining machine learning with opponent modeling techniques towards the design of computer agents for negotiating with people in settings of incomplete information.
AB - Many negotiations in the real world are characterized by incomplete information, and participants' success depends on their ability to reveal information in a way that facilitates agreement without compromising the individual gains of agents. This paper presents a novel agent design for repeated negotiation in incomplete information settings that learns to reveal information strategically during the negotiation process. The agent used classical machine learning techniques to predict how people make and respond to offers during the negotiation, how the y reveal information and their response to potential revelation actions by the agent. The agent was evaluated empirically in an extensive empirical study spanning hundreds of human subjects. Results show that the agent was able to outperform people. In particular, it learned (1) to make offers that were beneficial to people while not compromising its own benefit; (2) to incrementally reveal information to people in a way that increased its expected performance. The approach generalizes to new settings without the need to acquire additional data. This work demonstrates the efficacy of combining machine learning with opponent modeling techniques towards the design of computer agents for negotiating with people in settings of incomplete information.
UR - http://www.scopus.com/inward/record.url?scp=84893386029&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84893386029
SN - 9781577356158
T3 - Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
SP - 789
EP - 795
BT - Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
T2 - 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Y2 - 14 July 2013 through 18 July 2013
ER -