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 o#ered bundles. The price, quality, and content of the delivered goods may, for example, di#er based on daily dynamics and personal interest of customers.

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have nonlinear preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmark.

This paper presents an agent strategy for complex bilateral negotiations over many issues with inter-dependent valuations. We use ideas inspired by graph theory and probabilistic influence networks to derive efficient heuristics for negotiations about multiple issues. Experimental results show — under relatively weak assumptions with respect to the structure of the utility functions – that the developed approach leads to Pareto-efficient outcomes. Moreover, Pareto-efficiency can be reached with few negotiation steps, because we explicitly model and utilize the underlying graphical structure of complex utility functions. Consequently, our approach is applicable to domains where reaching an efficient outcome in a limited amount of time is important. Furthermore, unlike other solutions for high-dimensional negotiations, the proposed approach does not require a mediator.