  We present a machine learning-powered iterative combinatorial auction (MLCA).
The main goal of integrating machine learning (ML) into the auction is to
improve preference elicitation, which is a major challenge in large
combinatorial auctions (CAs). In contrast to prior work, our auction design
uses value queries instead of prices to drive the auction. The ML algorithm is
used to help the auction decide which value queries to ask in every iteration.
While using ML inside a CA introduces new challenges, we demonstrate how we
obtain a design that is individually rational, satisfies no-deficit, has good
incentives, and is computationally practical. We benchmark our new auction
against the well-known combinatorial clock auction (CCA). Our results indicate
that, especially in large domains, MLCA can achieve significantly higher
allocative efficiency than the CCA, even with only a small number of value
queries.
