Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Strategic negotiation in multiagent environments
Strategic negotiation in multiagent environments
Learning an Agent's Utility Function by Observing Behavior
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Optimal agendas for multi-issue negotiation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
An agenda-based framework for multi-issue negotiation
Artificial Intelligence
Motivation-Based Selection of Negotiation Partners
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Predicting partner's behaviour in agent negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Predicting people's bidding behavior in negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
On possibilistic case-based reasoning for selecting partners in multi-agent negotiation
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Expectation of trading agent behaviour in negotiation of electronic marketplace
Web Intelligence and Agent Systems
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Prediction partners' behaviors in negotiation has been an active research direction in recent years. By employing the estimation results, agents can modify their own ways in order to achieve an agreement much quicker or to look after much higher benefits for themselves. Some of estimation strategies have been proposed by researchers to predict agents' behaviors, and most of them are based on machine learning mechanisms. However, when the application domains become open and dynamic, and agent relationships are complicated, it is difficult to train data which can be used to predict all potential behaviors of all agents in a multi-agent system. Furthermore because the estimation results may have errors, a single result maybe not accurate and practical enough in most situations. In order to address these issues mentioned above, we propose a power regression analysis mechanism to predict partners' behaviors in this paper. The proposed approach is based only on the history of the offers during the current negotiation and does not require any training process in advance. This approach can not only estimate a particular behavior, but also an interval of behaviors according to an accuracy requirement. The experimental results illustrate that by employing the proposed approach, agents can gain more accurate estimation results on partners' behaviors by comparing with other two estimation functions.