Using Clustering Techniques to Improve Fuzzy Constraint Based Automated Purchase Negotiations

Abstract

Fuzzy constraint based approaches to automated negotiation provide a negotiation framework that has been applied in automated purchase negotiation scenarios. One of the key issues that these negotiation scenarios may have to address is the inclusion of catalogue of products in the negotiation model. To this end, this chapter presents a fuzzy constraint based negotiation framework, applicable in electronic market scenarios, where seller agents own private catalogues of products, and buyer agents model their preferences by means of fuzzy constraints. In the negotiation model proposed, interactions among agents are formalized as a dialogue game protocol, where the key mechanism is the use of detailed relaxation requests. The objective of a relaxation request is to conduct the negotiation dialogue to an optimal search space. However, the generation of relaxation requirements is difficult to manage, and involves several input parameters that must be considered. A novel mechanism is proposed in order to generate relaxation requests, that is based on the use of clustering applied over the catalogue of products. We show how the performance of the negotiation processes in terms of computation time and joint utility can be improved. Specifically, via empirical evaluation, the negotiation algorithm can lead to a 35% improvement in the duration of the negotiation dialogues, and to a significant improvement in the utility of the deals that are made.