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.
2nd International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2003 and the 5th Workshop on Agent-Mediated Electronic Commerce, AMEC 2003
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

Somefun, D.J.A, Gerding, E.H, Bohte, S.M, & La Poutré, J.A. (2004). Automated negotiation and bundling of information goods. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (pp. 1–17). doi:10.1007/978-3-540-25947-3_1