An empirical study of interest-based negotiation
Proceedings of the ninth international conference on Electronic commerce
An agent architecture for multi-attribute negotiation using incomplete preference information
Autonomous Agents and Multi-Agent Systems
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Simultaneously modeling humans' preferences and their beliefs about others' preferences
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Opponent modelling in automated multi-issue negotiation using Bayesian learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Hi-index | 0.00 |
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 they 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 (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 agent also had a positive effect on people's strategy, in that people playing the agent performed significantly higher than people playing other people. 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.