论文标题
IMLCA：机器学习驱动的迭代式组合拍卖与间隔竞标
iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding
论文作者
论文摘要
在大型组合拍卖中，偏好启发是一个主要的挑战，因为捆绑空间在项目数量中呈指数增长。最近的工作使用机器学习（ML）算法来识别一小部分捆绑包来查询每个出价者。但是，这项先前工作的缺点是投标人必须为查询捆绑包提交确切的值，这可能很昂贵。为了解决这个问题，我们提出了IMLCA，这是一种新的ML迭代迭代组合拍卖，并带有间隔竞标（即投标人提交上限和下限而不是精确值）。为了将拍卖引导到有效的分配中，我们引入了基于价格的活动规则，要求竞标者仅收紧相关捆绑包的界限。在我们的实验中，IMLCA达到的分配效率与使用精确招标的先前基于ML的拍卖相同。此外，它的表现要优于现实尺寸的域中众所周知的组合时钟拍卖。
Preference elicitation is a major challenge in large combinatorial auctions because the bundle space grows exponentially in the number of items. Recent work has used machine learning (ML) algorithms to identify a small set of bundles to query from each bidder. However, a shortcoming of this prior work is that bidders must submit exact values for the queried bundles, which can be quite costly. To address this, we propose iMLCA, a new ML-powered iterative combinatorial auction with interval bidding (i.e., where bidders submit upper and lower bounds instead of exact values). To steer the auction towards an efficient allocation, we introduce a price-based activity rule, asking bidders to tighten bounds on relevant bundles only. In our experiments, iMLCA achieves the same allocative efficiency as the prior ML-based auction that uses exact bidding. Moreover, it outperforms the well-known combinatorial clock auction in a realistically-sized domain.
