Preference Elicitation and Winner Determination in Multi-Attribute Auctions

Multi-Attribute Reverse Auctions (MARAs) are excellent protocols to automate negotiation among sellers. Eliciting the buyer0s preferences and determining the winner are both challenging problems for MARAs. To solve these problems, we propose two algorithms namely MAUT* and CP-net*, which are respectively the improvement of the Multi-Attribute Utility Theory (MAUT) and constrained CP-net. The buyers can now express conditional, qualitative as well as quantitative preferences over the item attributes. To evaluate the performance in time of the proposed algorithms, we conduct an experimental study on several problem instances. The results favor MAUT* in most of the cases.