kernel density estimation

With the emergence of the World Wide Web, Electronic Commerce (E-commerce) has been growing rapidly in the past two decades. Intelligent agents play the main role in making the negotiation between different entities automatically.  Automated negotiation allows resolving opponent agents' mutual concerns to reach an agreement without the risk of losing individual profits. However, due to the unknown information about the opponent's strategies, automated negotiation is difficult. The main challenge is how to reveal the optimal information about the opponent's strategy during the negotiation process to propose the best counter-offer. In this paper, we design a buyer agent which can automatically negotiate with the opponent using artificial intelligence techniques and machine learning methods. The proposed buyer agent is designed to learn the opponent's strategies during the negotiation process using four methods "Bayesian Learning", "Kernel Density Estimation", "Multilayer Perceptron Neural Network", and "Nonlinear Regression". Experimental results show that the use of machine learning methods increases the negotiation efficiency, which is measured and evaluated by parameters such as the rate agreement (RA), average buyer utility (ABU), average seller utility (ASU), average rounds (AR). Rate agreement and average buyer utility have increased from 58% to 74% and 90% to 94%, respectively, and average rounds have decreased from 10% to 0.04%.

A main problem in vector quantization (VQ) is codebook designation. The traditional method used for VQ codebook generation, is the Generalized Lloyd Algorithm (GLA). The efficiency of the GLA algorithm is hardly dependent on the initial codebook selection. But, GLA algorithm usually gets trapped into local minimum of distortion, resulting in a random codebook initialization. In this paper, an effective codebook initialization algorithm based on Kernel density estimation has been proposed. Experimental results show that the proposed algorithm not only improves the quality of generated codebook but decreases the computation time compared to the GLA algorithm.