Graphical utility models represent powerful formalisms for modeling complex agent decisions involving multiple issues. in the context of negotiation, our previous work  has shown that using utility graphs enables agents to reach pareto-efficient agreements with a limited number of steps in high-dimensional negotiations involving non-linear dependencies. this paper considerably extends the results of , by proposing a method for constructing the utility graphs of buyers automatically, based on previous negotiation data. the proposed method is based on techniques inspired from item-based collaborative filtering, used in on-line recommendation algorithms. experimental results show that our approach is able to retrieve the structure of utility graphs on-line, with a relatively high degree of accuracy, even if a relatively small amount of data about concluded negotiations is available. furthermore, we provide a rigorous method for determining the cut-off number of edges for a wide category of random graphs.
|Journal||Belgian/Netherlands Artificial Intelligence Conference|
|Publication status||Published - 1 Dec 2006|
|Event||18th Belgium-Netherlands Conference on Artificial Intelligence, BNAIC 2006 - Namur, Belgium|
Duration: 5 Oct 2006 → 6 Oct 2006