Predicting people's bidding behavior in negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning social preferences in games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Agent-human interactions in the continuous double auction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Modeling how humans reason about others with partial information
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
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
Incorporating BDI Agents into Human-Agent Decision Making Research
ESAW '09 Proceedings of the 10th International Workshop on Engineering Societies in the Agents World X
Agent decision-making in open mixed networks
Artificial Intelligence
An Adaptive Agent for Negotiating with People in Different Cultures
ACM Transactions on Intelligent Systems and Technology (TIST)
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Many environments in which people and computer agents interact involve deploying resources to accomplish tasks and satisfy goals. This paper investigates the way that the context in which decisions are made affects the behavior of people and the performance of computer agents that interact with people in such environments. It presents experiments that measured negotiation behavior in two different types of settings. One setting was a task context that made explicit the relationships among goals, (sub)tasks and resources. The other setting was a completely abstract context in which only the payoffs for the decision choices were listed. Results show that people are more helpful, less selfish, and less competitive when making decisions in task contexts than when making them in completely abstract contexts. Further, their overall performance was better in task contexts. A predictive computational model that was trained on data obtained in the task context outperformed a model that was trained under the abstract context. These results indicate that taking context into account is essential for the design of computer agents that will interact well with people.