AutONA: a system for automated multiple 1-1 negotiation
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The Influence of Social Dependencies on Decision-Making: Initial Investigations with a New Game
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Adapting to agents' personalities in negotiation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Efficient agents for cliff-edge environments with a large set of decision options
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Using multiagent teams to improve the training of incident commanders
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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
Mobile opportunistic commerce: mechanisms, architecture, and application
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
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
The handbook of negotiation and culture
The handbook of negotiation and culture
Investigating the benefits of automated negotiations in enhancing people's negotiation skills
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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
Using focal point learning to improve tactic coordination in human-machine interactions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Can automated agents proficiently negotiate with humans?
Communications of the ACM - Amir Pnueli: Ahead of His Time
Agent-human interactions in the continuous double auction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
The influence of task contexts on the decision-making of humans and computers
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Agent decision-making in open mixed networks
Artificial Intelligence
Human-robot interaction in rescue robotics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A case study in model selection for policy engineering: simulating maritime customs
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
A cultural sensitive agent for human-computer negotiation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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The rapid dissemination of technology such as the Internet across geographical and ethnic lines is opening up opportunities for computer agents to negotiate with people of diverse cultural and organizational affiliations. To negotiate proficiently with people in different cultures, agents need to be able to adapt to the way behavioral traits of other participants change over time. This article describes a new agent for repeated bilateral negotiation that was designed to model and adapt its behavior to the individual traits exhibited by its negotiation partner. The agent’s decision-making model combined a social utility function that represented the behavioral traits of the other participant, as well as a rule-based mechanism that used the utility function to make decisions in the negotiation process. The agent was deployed in a strategic setting in which both participants needed to complete their individual tasks by reaching agreements and exchanging resources, the number of negotiation rounds was not fixed in advance and agreements were not binding. The agent negotiated with human subjects in the United States and Lebanon in situations that varied the dependency relationships between participants at the onset of negotiation. There was no prior data available about the way people would respond to different negotiation strategies in these two countries. Results showed that the agent was able to adopt a different negotiation strategy to each country. Its average performance across both countries was equal to that of people. However, the agent outperformed people in the United States, because it learned to make offers that were likely to be accepted by people, while being more beneficial to the agent than to people. In contrast, the agent was outperformed by people in Lebanon, because it adopted a high reliability measure which allowed people to take advantage of it. These results provide insight for human-computer agent designers in the types of multicultural settings that we considered, showing that adaptation is a viable approach towards the design of computer agents to negotiate with people when there is no prior data of their behavior.