Learning Methodologies to Support E-Business in the Automated Negotiation Process

Abstract

The automated negotiation performed by a software agent is investigated in order to improve the benefits compared to a humane face-to-face negotiation. The profitability of e-business applications can be increased by the support of automated negotiation tools. This research proposes a set of learning methodologies to support both the suppliers’ and customers’ negotiation activities. The learning methodologies are based on Q-learning technique, which is able to evaluate the utility of the actions without a model of the environment. The context regards one-to-many negotiation and multi-issues (volume, price, and due date). A simulation environment is developed to test the proposed methodologies and evaluate the benefits compared to a negotiation approach without learning support tool. The simulations are conducted in several market conditions, and a proper statistical analysis is performed. The simulation results show that the proposed methodologies lead to benefits both for suppliers and customers when both the opponents adopt the learning approach.