Predicting opponent's moves in electronic negotiations using neural networks

Electronic negotiation experiments provide a rich source of information about relationships between the negotiators, their individual actions, and the negotiation dynamics. This information can be effectively utilized by intelligent agents equipped with adaptive capabilities to learn from past negotiations and assist in selecting appropriate negotiation tactics. This paper presents an approach to modeling the negotiation process in a timeseries fashion using artificial neural network. In essence, the network uses information about past offers and the current proposed offer to simulate expected counteroffers. On the basis of the model's prediction, "whatif" analysis of counteroffers can be done with the purpose of optimizing the current offer. The neural network has been trained using the LevenbergMarquardt algorithm with Bayesian Regularization. The simulation of the predictive model on a testing set has very good and highly signifi...

• Type: Journal
• Year: 2008
• Where: ESWA
• Authors: Réal Carbonneau, Gregory E. Kersten, Rustam M. Vahidov
• Keywords: Electronic Negotiations, ESWA 2006, ESWA 2008, Intelligent Agent, Negotiation