论文标题
流式自适应下调最大化
Streaming Adaptive Submodular Maximization
论文作者
论文摘要
许多顺序决策问题都可以作为自适应的下管性最大化问题。但是，该领域中的大多数现有研究都集中在基于池的设置上，其中一个人可以按任何顺序挑选项目，而基于流的设置的研究很少，其中项目以任意顺序到达，并且必须立即决定是否在到达时选择项目。在本文中，我们介绍了一类新的实用程序功能，即半准时函数。我们开发了一系列有效的算法，以最大程度地提高基于流的设置下的半循环次数函数。
Many sequential decision making problems can be formulated as an adaptive submodular maximization problem. However, most of existing studies in this field focus on pool-based setting, where one can pick items in any order, and there have been few studies for the stream-based setting where items arrive in an arbitrary order and one must immediately decide whether to select an item or not upon its arrival. In this paper, we introduce a new class of utility functions, semi-policywise submodular functions. We develop a series of effective algorithms to maximize a semi-policywise submodular function under the stream-based setting.
