Complex processes in science and engineering are often formulated as multistage decision-making problems. In this paper, we consider a type of multistage decision-making process called a cascade process. A cascade process is a multistage process in which the output of one stage is used as an input for the subsequent stage. When the cost of each stage is expensive, it is difficult to search for the optimal controllable parameters for each stage exhaustively. To address this problem, we formulate the optimization of the cascade process as an extension of the Bayesian optimization framework and propose two types of acquisition functions based on credible intervals and expected improvement. We investigate the theoretical properties of the proposed acquisition functions and demonstrate their effectiveness through numerical experiments. In addition, we consider an extension called suspension setting in which we are allowed to suspend the cascade process at the middle of the multistage decision-making process that often arises in practical problems. We apply the proposed method in a test problem involving a solar cell simulator, which was the motivation for this study.
翻译:科学和工程的复杂过程往往被发展成多阶段决策的问题。在本文件中,我们考虑了一种称为级联过程的多阶段决策程序。级联过程是一个多阶段过程,将一个阶段的产出用作下一个阶段的投入。当每个阶段的费用昂贵时,很难为每个阶段寻找最佳的可控制参数。为了解决这个问题,我们将级联过程的优化作为巴耶斯优化框架的延伸,并根据可信的间隔和预期的改进提出两种类型的购置功能。我们调查拟议的购置功能的理论特性,并通过数字实验表明其有效性。此外,我们考虑一个称为暂停的延长设置,允许我们在经常出现实际问题的多阶段决策进程的中间暂停级联进程。我们在涉及太阳电池模拟器的试验问题中应用拟议的方法,这是本研究的动机。</s>