Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging
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DOI：
10.1109/access.2020.2980228
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发表时间：
2020-02
期刊：
影响因子：
3.9
通讯作者：
Chi Zhang;Zeyu Wang;A. Shafieezadeh
中科院分区：
文献类型：
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作者：
Chi Zhang;Zeyu Wang;A. Shafieezadeh
许多现象的巨大不确定性对决策提出了挑战。收集附加信息以更好地描述可约不确定性是决策备选方案之一。信息价值（VoI）分析是一种数学决策框架，它量化新数据的预期潜在收益，并协助信息收集资源的最佳分配。然而，由于潜在的贝叶斯推理，特别是对于相等类型的信息，对VoI的分析计算成本非常高。本文提出了第一个基于代理的VoI分析框架。代替了在基于代理模型的可靠性方法中通常追求的描述决策感兴趣事件的极限状态函数的建模，提出的框架对系统响应建模。这种方法提供了从代理模型之间的观察中共享相等型信息，以更新多个感兴趣事件的可能性。此外，提出了模型和训练点共享两种知识共享方案，以最有效地利用昂贵的模型评估提供的知识。这两种方案结合了基于错误率的自适应训练方法，有效地生成准确的克里格代理模型。将提出的VoI分析框架应用于桁架桥梁荷载试验的最优决策问题。虽然基于重要性采样和自适应Kriging蒙特卡罗模拟的最先进方法无法解决这一问题，但所提出的方法被证明可以在有限数量的模型评估下提供准确和稳健的VoI估计。因此，该方法有利于VoI在复杂决策问题中的应用。
Large uncertainties in many phenomena have challenged decision making. Collecting additional information to better characterize reducible uncertainties is among decision alternatives. Value of information (VoI) analysis is a mathematical decision framework that quantifies expected potential benefits of new data and assists with optimal allocation of resources for information collection. However, analysis of VoI is computational very costly because of the underlying Bayesian inference especially for equality-type information. This paper proposes the first surrogate-based framework for VoI analysis. Instead of modeling the limit state functions describing events of interest for decision making, which is commonly pursued in surrogate model-based reliability methods, the proposed framework models system responses. This approach affords sharing equality-type information from observations among surrogate models to update likelihoods of multiple events of interest. Moreover, two knowledge sharing schemes called model and training points sharing are proposed to most effectively take advantage of the knowledge offered by costly model evaluations. Both schemes are integrated with an error rate-based adaptive training approach to efficiently generate accurate Kriging surrogate models. The proposed VoI analysis framework is applied for an optimal decision-making problem involving load testing of a truss bridge. While state-of-the-art methods based on importance sampling and adaptive Kriging Monte Carlo simulation are unable to solve this problem, the proposed method is shown to offer accurate and robust estimates of VoI with a limited number of model evaluations. Therefore, the proposed method facilitates the application of VoI for complex decision problems.