Experimental Investigation of Quasi-Newton Approaches to a Learning Problem in Electronic Negotiation

Abstract

The recent growth in electronic commerce has motivated the development of semi-autonomous negotiation systems capable of implementing multiple negotiations simultaneously. Different approaches have recently been presented in the literature with the aim of providing a solution to this growing market segment. The current thesis presents an examination of optimization approaches for learning the parameters of a time-dependent decision-function that has recently obtained significant interest in the negotiation literature. Twelve different nonlinear optimization variants are evaluated using 800 problems, and the resulting 9600 runs are statistically analyzed on four different performance measures. Potential implications of our analysis are discussed for their possible use in the context of electronic negotiation