Online decision making plays a crucial role in numerous real-world
applications. In many scenarios, the decision is made based on performing a
sequence of tests on the incoming data points. However, performing all tests
can be expensive and is not always possible. In this paper, we provide a novel
formulation of the online decision making problem based on combinatorial
multi-armed bandits and take the cost of performing tests into account. Based
on this formulation, we provide a new framework for cost-efficient online
decision making which can utilize posterior sampling or BayesUCB for
exploration. We provide a rigorous theoretical analysis for our framework and
present various experimental results that demonstrate its applicability to
real-world problems.

本文基于组合多臂赌博机，考虑了测试成本，提供了一种新的成本高效的在线决策框架，并通过后验抽样或 BayesUCB 进行探索。我们对该框架进行了严格的理论分析，并提供了多个实验结果，证明了它在实际问题中的适用性。

低成本在线决策：一种组合多臂赌博机方法

Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed  Bandit Approach

A fundamental problem in artificial intelligence is that nobody really knows
what intelligence is. The problem is especially acute when we need to consider
artificial systems which are significantly different to humans. In this paper
we approach this problem in the following way: We take a number of well known
informal definitions of human intelligence that have been given by experts, and
extract their essential features. These are then mathematically formalised to
produce a general measure of intelligence for arbitrary machines. We believe
that this equation formally captures the concept of machine intelligence in the
broadest reasonable sense. We then show how this formal definition is related
to the theory of universal optimal learning agents. Finally, we survey the many
other tests and definitions of intelligence that have been proposed for
machines.

本文旨在通过数学公式将各种形式的人工智能定义转化为机器学习中的智能度量，并将之与通用最优学习智能体的理论联系起来，最后综述了已经为机器学习提出的智能度量和测试方法。