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 time-series fashion using artificial neural network. In essence, the network uses information about past offers and the current proposed offer to simulate expected counter-offers. On the basis of the model's prediction, ''what-if'' analysis of counter-offers can be done with the purpose of optimizing the current offer. The neural network has been trained using the Levenberg-Marquardt algorithm with Bayesian Regularization. The simulation of the predictive model on a testing set has very good and highly significant performance. The findings suggest that machine learning techniques may find useful applications in the context of electronic negotiations. These techniques can be effectively incorporated in an intelligent agent that can sense the environment and assist negotiators by providing predictive information, and possibly automating some negotiation steps.