An adaptive fuzzy logic system for automated negotiations

The rapid growth of the Web means that humans become increasingly incapable of searching among millions of resources to find and purchase items. Autonomous entities such as agents could help in these situations. Electronic markets (EMs) are virtual sites where these autonomous entities can interact to exchange items and obtain specific returns. In this study, we consider the interactions between buyers and sellers in EMs, where we focus specifically on the buyer side. These interactions can be modeled as finite horizon negotiations. However, the buyer cannot be certain of the characteristics of the seller during negotiations (incomplete knowledge). Thus, to address this uncertainty, we propose a fuzzy logic (FL) system that is responsible for determining the appropriate actions of the buyer during every negotiation round. We also propose an adaptation technique that updates the FL rule base and system membership functions as necessary. Using this approach, the system can respond to even the complex strategies followed by a seller. A seller strategy estimation method is also adopted by the system, which employs the known kernel density estimator (KDE). We provide results for a large number of negotiations and compare our system with previous research in this area. Our results show that the proposed system exhibits good performance in many negotiation scenarios