AutONA: a system for automated multiple 1-1 negotiation
Proceedings of the 4th ACM conference on Electronic commerce
An agent architecture for multi-attribute negotiation using incomplete preference information
Autonomous Agents and Multi-Agent Systems
Resolving crises through automated bilateral negotiations
Artificial Intelligence
Facing the challenge of human-agent negotiations via effective general opponent modeling
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Modeling reciprocal behavior in human bilateral negotiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Agent decision-making in open mixed networks
Artificial Intelligence
Using aspiration adaptation theory to improve learning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
An Adaptive Agent for Negotiating with People in Different Cultures
ACM Transactions on Intelligent Systems and Technology (TIST)
Enabling generative, emergent artificial culture
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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People's cultural background has been shown to affect the way they reach agreements in negotiation and how they fulfill these agreements. This paper presents a novel agent design for negotiating with people from different cultures. Our setting involved an alternating-offer protocol that allowed parties to choose the extent to which they kept each of their agreements during the negotiation. A challenge to designing agents for such setting is to predict how people reciprocate their actions over time despite the scarcity of prior data of their behavior across different cultures. Our methodology addresses this challenge by combining a decision theoretic model with classical machine learning techniques to predict how people respond to offers, and the extent to which they fulfill agreements. The agent was evaluated empirically by playing with 157 people in three countries---Lebanon, the U. S., and Israel---in which people are known to vary widely in their negotiation behavior. The agent was able to outperform people in all countries under conditions that varied how parties depended on each other at the onset of the negotiation. This is the first work to show that a computer agent can learn to outperform people when negotiating in three countries representing different cultures.