Knowledge-empowered automated negotiation system for e-Commerce

This paper focuses on knowledge empowered automated negotiation systems for buyer-centric multi-bilateral multi-attribute e-Procurement. We propose two knowledge empowered models, namely KERM and KACM. KERM is used for the buyer to determine a list of suppliers which are the best qualified candidates to negotiate with. The use of knowledge features largely in the model, which incorporates both the buyer's and supplier's profiles in evaluating a quote. Historical trade records of a supplier contribute to the supplier's profile credit and therefore the rank of the supplier's quote. KERM also allows the flexibility to assign appropriate weights, based on buyer's interests, to each knowledge factor affecting the overall evaluation result of a quote. The resulted list of quotes of high rank is believed to produce satisfactory negotiation result for the buyer. KACM enables an automated concession process, while at the same time facilitates a flexible negotiation via the use of concept switch and tagged rules. Different from other negotiation models, KACM emphasizes the utilization of knowledge originated from the historical negotiation data in estimating and fine-tuning the negotiation parameters, for improving the performance of automated negotiation. Graph results show that our software prototype system makes significant improvement in the satisfaction level of negotiation results.