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：我们研究了组合分配领域，包括组合拍卖和课程分配。该领域的主要挑战在于，组合空间随物品数量呈指数级增长。为了解决这一问题，近期多篇论文提出了基于机器学习的偏好 elicitation 算法，旨在仅从智能体处获取最关键的信息。然而，这些先前工作的主要缺陷在于，它们并未对机制在尚未 elicitation 的捆绑包上的价值不确定性进行建模。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，将一种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不确定性边界，该边界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 elicitation 阶段正确探索（而不仅仅是利用）组合空间。我们在多个频谱拍卖领域开展了计算实验，以评估 BOCA 的性能。我们的结果表明，BOCA 实现了比最先进方法更高的分配效率。