A Machine-Learning Approach to Automated Negotiation and Prospects for Electronic Commerce

We show how a system of artificial adaptive agents, using a genetic algorithm based learning technique, can learn strategies that enable it to effectively participate in stylized business negotiations. The negotiation policies learned are evaluated on several dimensions, including joint outcomes, nearness to the efficient frontier, and the similarity to outcomes of human negotiations. The results are promising for integrating such agents into practicable electronic commerce systems. We discuss what a system might look like and ways in which particular classes of business negotiations could be supported or even entirely automated.