We study the combinatorial assignment domain, which includes combinatorial auctions and course allocation. The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning-based preference elicitation algorithms that aim to elicit only the most important information from agents. However, the main shortcoming of this prior work is that it does not model a mechanism's uncertainty over values for not yet elicited bundles. In this paper, we address this shortcoming by presenting a Bayesian optimization-based combinatorial assignment (BOCA) mechanism. Our key technical contribution is to integrate a method for capturing model uncertainty into an iterative combinatorial auction mechanism. Concretely, we design a new method for estimating an upper uncertainty bound that can be used to define an acquisition function to determine the next query to the agents. This enables the mechanism to properly explore (and not just exploit) the bundle space during its preference elicitation phase. We run computational experiments in several spectrum auction domains to evaluate BOCA's performance. Our results show that BOCA achieves higher allocative efficiency than state-of-the-art approaches.

翻译：我们研究组合分配领域，包括组合拍卖和课程分配。该领域的主要挑战在于，随着物品数量的增加，组合空间呈指数级增长。为解决这一问题，近年来有多篇论文提出了基于机器学习的偏好诱导算法，旨在仅从智能体处获取最关键的信息。然而，先前工作的主要缺陷在于，它未对机制中尚未被诱导的组合价值不确定性建模。在本文中，我们通过提出一种基于贝叶斯优化的组合分配（BOCA）机制来弥补这一缺陷。我们的关键技术贡献在于将捕捉模型不确定性的方法集成到迭代组合拍卖机制中。具体而言，我们设计了一种新方法来估计上置信界限，该界限可用于定义采集函数，以确定向智能体提出的下一个查询。这使得机制能够在偏好诱导阶段合理探索（而不仅仅是利用）组合空间。我们在多个频谱拍卖领域进行计算实验，以评估BOCA的性能。结果表明，与最先进的方法相比，BOCA实现了更高的分配效率。