Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
University of Liege（列日大学）；Delft University of Technology（代尔夫特理工大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
Pablo G. Morato, Charalampos P. Andriotis, Konstantinos G. Papakonstantinou, Philippe Rigo
发表信息：
Reliability Engineering & System Safety (2023), 235, 109144
In the context of modern environmental and societal concerns, there is an increasing demand for methods able to identify management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I&M decision problem to the component level due to the computational complexity associated with global optimization methodologies under joint system-level state descriptions. In this paper, we propose an efficient algorithmic framework for inference and decision-making under uncertainty for engineering systems exposed to deteriorating environments, providing optimal management strategies directly at the system level. In our approach, the decision problem is formulated as a factored partially observable Markov decision process, whose dynamics are encoded in Bayesian network conditional structures. The methodology can handle environments under equal or general, unequal deterioration correlations among components, through Gaussian hierarchical structures and dynamic Bayesian networks. In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach, in which the policies are approximated by actor neural networks guided by a critic network. By including deterioration dependence in the simulated environment, and by formulating the cost model at the system level, DDMAC policies intrinsically consider the underlying system-effects. This is demonstrated through numerical experiments conducted for both a 9-out-of-10 system and a steel frame under fatigue deterioration. Results demonstrate that DDMAC policies offer substantial benefits when compared to state-of-the-art heuristic approaches. The inherent consideration of system-effects by DDMAC strategies is also interpreted based on the learned policies.
Colored Markov Random Fields for Probabilistic Topological Modeling
Statistical Sciences Dept., Sapienza University of Rome, Italy（统计科学系，罗马萨皮恩扎大学）；DIET Dept., Sapienza University of Rome, Italy（DIET系，罗马萨皮恩扎大学）；DIAG Dept., Sapienza University of Rome, Italy（DIAG系，罗马萨皮恩扎大学）
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing
University of California, Los Angeles（加州大学洛杉矶分校）；University of Trento（特伦托大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
加权模型集成（Weighted Model Integration，简称WIM）是一个非常吸引人的概率推理框架：它允许通过可满足性模理论（Satisfiability Modulo Theories，简称SMT）的语言来表达变量既连续又离散的真实世界问题的复杂依赖关系，以及计算具有复杂逻辑和算术约束的概率查询。然而，现有的非线性规划求解器还没有准备好扩展到这些问题。它们要么完全忽略了问题的内在依赖结构，要么局限于限制性太强的结构。为了缩小这一差距，我们推导出一个因式分解的形式主义，使我们能够设计一个可扩展的基于消息传递，MP-BSSOVER。也就是说，MP-SVM是第一个SVM求解器，它允许：1）对整个树结构的SVM问题进行精确推理; 2）在线性时间内计算所有边缘密度; 3）摊销查询间的推理。实验结果表明，我们的求解器显着优于现有的非线性规划求解器在一个大的基准集。
Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits
Università degli Studi di Brescia（布雷西亚大学）；Cardiff University（卡迪夫大学）；CCDC Army Research Laboratory（CCDC陆军研究实验室）；KU Leuven（鲁汶大学）；Blue Prism AI Labs（蓝棱镜人工智能实验室）；Ozyegin University（奥兹耶金大学）
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing
University of California, LA（加利福尼亚大学洛杉矶分校）；AMSS（数学与系统科学研究院）；Chinese Academy of Sciences（中国科学院）；DISI（信息工程与计算机科学系）；University of Trento（特伦托大学）
加权模型集成（Weighted Model Integration，简称WMM）是一个非常吸引人的概率推理框架：它允许通过可满足性模理论（Satisfiability Modulo Theories，简称SMT）的语言来表达真实世界混合场景的复杂依赖关系，其中变量在本质上是异构的（包括连续和离散）;以及计算具有任意复杂逻辑约束的概率查询。最近的工作表明，在某些假设下，可简化的模型集成（MI）问题，从而有效地允许混合概率推理的体积计算。在本文中，我们介绍了一种新的制定MI通过消息传递计划，允许有效地计算的边际密度和统计矩的所有变量在线性时间。因此，当任意丰富的MI查询符合问题结构时，我们能够摊销推理，这里表示为与SMT公式相关联的原始图。此外，我们从理论上跟踪的易处理边界的确切MI。事实上，我们证明，在原始图的结构要求，使我们的MI算法易于处理-边界的直径和树宽-边界不仅是足够的，但通过MI易于推理的必要条件。
Probabilistic Multi-Regional Solar Power Forecasting with Any-Quantile Recurrent Neural Networks
Faculty of Electrical Engineering, Czestochowa University of Technology（捷斯托霍瓦技术大学电气工程学院）；Faculty of Mathematics and Computer Science, University of Łódź（卢布林大学数学与计算机科学学院）；CAMINO - Centre for Data Analysis, Modelling and Computational Sciences, University of Łódź（卢布林大学CAMINO数据与建模计算科学中心）
