We present a visualization of bargaining processes between a seller and a buyer (or buyers) where negotiations take place over the composition and price of bundles of goods. The negotiations themselves are simulated by software specific for the kind of negotiations. The visualization collects information from the simulation steps in a simulation-specific way and visualizes those steps in a general way. We demonstrate the visualization for two cases: a seller who offers bundles of linearly dependent items to many customers and a seller who repeatedly offers a bundle of non linearly dependent goods to a specific customer meanwhile learning this customers preferences.

This paper presents an agent strategy for automated negotiation over a set of issues with interdependent valuations. First, we show how complex utility functions over a set of binary issues (or bundle of items) can be concisely represented using the formalism of utility graphs. Next, a heuristic is proposed for automated learning of an opponent's preference function, starting from a given (maximal) factorization of this function as a utility graph. The contribution of our approach is that it speeds up learning considerably compared to other techniques proposed in existing literature for this problem. An extended version of this paper is given in [1], while [2] provides a further extension of this work, in which collaborative filtering is used to learn the starting structure of such graphs.