Thesis (Ph. D.)--University of Florida, 2001. / Title from title page of source document. Document formatted into pages; contains xi, 117 p.; also contains graphics. Includes vita. Includes bibliographical references.

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Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 157-160).

Thesis (doctoral)--Universität, Münster, 2004. / Description based on print version record. Includes bibliographical references.

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

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

It is getting more important to have quick response during the negotiation process due to the very competitive environment. However, in the negotiation process, it is quite often that negotiators do not have enough time to understand the negotiation issue, the available alternatives and their priorities. In addition, negotiators may also have difficulties to catch path of interactions between negotiators, i.e. negotiation dance. These problems may have negative impact on the negotiation results or even break the negotiation. The goal of this research is to help negotiators with clearer picture about the negotiation issues and the interactions between negotiators in order to have better negotiation decisions during the negotiation process. First, the challenges in understanding the negotiation issues and the interactions between negotiators are identified. Then, based on the identified challenges, five negotiation decision support agents are designed. The algorithm of each agent is proposed too. In order to evaluate the effectiveness of the proposed agents, prototypes of two agents have been developed and evaluated through an experiment. The result is positive.