On the use of particle swarm optimization and Kernel density estimator in concurrent negotiations

Electronic Marketplaces (EMs) can offer a number of advantages for users searching for products. In EMs, Intelligent Agents (IAs) can undertake the responsibility of representing buyers and sellers and negotiate over the conclusion of purchases. For this purpose, a negotiation is held between IAs. The most important characteristics are the deadline and the pricing strategy. The strategy defines the proposed prices at every round of the negotiation. We focus on the buyer side. We study concurrent negotiations between a buyer and a set of sellers. In this setting, the buyer utilizes a number of threads. Each thread follows a specific strategy and adopts swarm intelligence techniques for achieving the optimal agreement. The Particle Swarm Optimization (PSO) algorithm is adopted by each thread. Our architecture requires no central coordination. In real situations, there is absolutely no knowledge for the characteristics of the involved entities. In this paper, we model such kind of uncertainty through known techniques for estimating the distribution of deadlines and strategies. One of them is the Kernel Density Estimation (KDE) technique. Our experimental results depict the time interval where the agreement is possible and the efficiency of the proposed model.