Predicting Opponents Offers in Multi-agent Negotiations Using ARTMAP Neural Network

Negotiations are one of the most common ways that agents in a multi-agent system use to reach agreements. As negotiations commonly are multi-lateral and multi-issue, these processes become more difficult. Moreover, in real-world applications in which real-time agents are needed, this issue becomes more important. Autonomous agents should be able to decide what to propose in each round of negotiations quickly. In this situation if an agent is able to predict opponent\´s behavior including its next offer, the task of offering comes to be more efficient. This paper presents an approach in which an agent can predict opponent\´s next offer using a history of previous offers and counter-offers by the aid of ARTMAP Neural Network. The agent can employ this information to determine its offer after a "what-if" analysis of possible offers. The experimental results show that this approach substantially decreases the duration of negotiations and can be used in real applications as well.