AUTOMATED NEGOTIATION BASED ON ENSEMBLE LEARNING AND OPTIMAL COUNTER PROPOSAL

Abstract

Agent-mediated automated negotiation is a key form of interaction in the e-commerce environment. Agents reach an agreement through an iterative process of making offers. However, agents are prone to conceal their private negotiation information, which decreases the efficiency of negotiation. In this paper, an ensemble learning-based negotiation method is proposed. The new method labels the proposals automatically by mining the implicit information in negotiation history data. Then, the labeled proposals become the training samples of the ensemble learning algorithm, which generates the estimation of the opponent’s utility function. At last, based on the utility function of both sides, a win-win negotiation counter-proposal is generated through a particle swarm optimization algorithm. The experimental results indicate the benefits and efficiency of the proposed method.