An Evolutionary Negotiation Model Using Genetic Network Programming
Md. Tofazzal Hossain, Shingo Mabu, and Kotaro Hirasawa
Graduate school of Information, Production, and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan
In a common two-party price negotiation, the buyer and the seller try to maximize inherently conflicting objectives regarding the price of a product. Generating an intelligent solution for both the buyer and the seller is often difficult, time-consuming and inefficient under such conditions. In this work, we therefore devise an intelligent solution for such complex situations in Business-to-Business E-business activities. The main contribution of this paper is the design of an intelligent decision support mechanism for an intelligent and automated negotiation solution based on a newly developed evolutionary computing technique called Genetic Network Programming (GNP). In addition, we devise a new Negotiation Protocol aiming to develop a set of rules for agents’ behavior during the negotiation process. Moreover, to reach a negotiation solution, concession that is usually predetermined in the conventional system is needed. To the contrary, in this research, concession is determined automatically by evolution that makes our system intelligent, automated, and efficient. Extensive simulations are emphasized to study the characteristics and behaviors of agents obtained by evolution.
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