In this paper, we present a novel system for selling bundles of news items. Through the system, customers bargain with the seller over the price and quality of the delivered goods. The advantage of the developed system is that it allows for a high degree of flexibility in the price, quality, and content of the offered bundles. The price, quality, and content of the delivered goods may, for example, differ based on daily dynamics and personal interests of customers. Autonomous software agents execute the negotiation on behalf of the users of the system. To perform the actual negotiation these agents make use of bargaining strategies. We decompose bargaining strategies into concession strategies and Pareto efficient search strategies. Additionally, we introduce the orthogonal and orthogonal-DF strategy: two Pareto search strategies. We show through computer experiments that the use of these Pareto search strategies will result in very efficient bargaining outcomes. Moreover, the system is set up such that it is actually in the best interest of the customer to have their agent adhere to this approach of disentangling the bargaining strategy.
|Title of host publication||Agent Mediated Electronic Commerce V (AMEC-V)|
|Editors||P. Faratin, D. Parkes, J. Rodriquez-Aguilar|
|Place of Publication||Berlin / Heidelberg|
|Publication status||Published - 2004|
|Name||Lecture Notes in Artificial Intelligence LNAI|