In many model-based diagnosis applications it is impossible to provide such a set of observations and/or measurements that allow to identify the real cause of a fault. Therefore, diagnosis systems often return many possible candidates, leaving the burden of selecting the correct diagnosis to a user. Sequential diagnosis techniques solve this problem by automatically generating a sequence of queries to some oracle. The answers to these queries provide additional information necessary to gradually restrict the search space by removing diagnosis candidates inconsistent with the answers.
During query computation, existing sequential diagnosis methods often require the generation of many unnecessary query candidates and strongly rely on expensive logical reasoners. We tackle this issue by devising efficient heuristic query search methods. The proposed methods enable for the first time a completely reasoner-free query generation while at the same time guaranteeing optimality conditions, e.g. minimal cardinality or best understandability, of the returned query that existing methods cannot realize. Hence, the performance of this approach is independent of the (complexity of the) diagnosed system. Experiments conducted using real-world problems show that the new approach is highly scalable and outperforms existing methods by orders of magnitude.
A Model-Based Active Testing Approach to Sequential Diagnosis
Delft University of Technology（代尔夫特理工大学）；University College Cork（科克大学学院）
基于模型的诊断推理往往会产生大量的诊断假设。通过考虑额外的观察（被动监测），测量额外的变量（探测）或执行额外的测试（顺序诊断/测试排序），可以减少诊断集。在本文中，我们将上述方法与自动测试模式生成（ATPG）和基于模型的诊断（MBD）的技术相结合，形成了一个名为FRACTAL（FRAamework for Active Testing AL出租）的框架。除了将系统连接到其环境的输入和输出之外，在主动测试中，我们还考虑可以向其提供测试向量序列的其他输入变量。我们解决了计算最优控制分配（定义在分形）的贪婪近似算法称为分形-G的计算困难的问题。我们比较减少剩余的最小基数诊断的FRACTAL-G的两个以上的分形算法：FRACTAL-ATPG和FRACTAL-P。FRACTAL-ATPG是基于ATPG和顺序诊断，而FRACTAL-P是基于探测，虽然不是一个积极的测试算法，提供了一个基线比较可达诊断的分形算法的数量的下限。我们经验性地评估三个分形算法的权衡进行广泛的实验ISCAS 85/74 XXX基准的组合电路。
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；School of Information Technology and Electrical Engineering, The University of Queensland（昆士兰大学信息技术与电气工程学院）
Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks
University of Michigan（密歇根大学）；University of Texas Medical Branch（德克萨斯大学医学部）；Institute for Translational Sciences（转化科学研究所）；Electrical Engineering and Computer Science（电气工程与计算机科学学院）
Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
ActiveMedAgent: Cost-Aware Trajectory Learning for Multimodal Medical Diagnosis
Washington University in St. Louis（圣路易斯华盛顿大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Arizona State University（亚利桑那州立大学）；Yale University（耶鲁大学）；Amazon（亚马逊公司）
RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）
GraD-IBD: Graph Representation Learning from Diagnosis Trajectories for Early Detection of Inflammatory Bowel Disease
Department of Surgery, Mayo Clinic, Rochester, MN, USA（外科部，梅奥诊所，罗切斯特，MN，美国）；Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA（健康保健交付科学中心，梅奥诊所，罗切斯特，MN，美国）；Division of Hepatobiliary and Pancreas Surgery, Mayo Clinic, Rochester, MN, USA（肝胆胰外科部，梅奥诊所，罗切斯特，MN，美国）；Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA（人工智能与信息学部，梅奥诊所，罗切斯特，MN，美国）
HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition
Technical University of Munich（慕尼黑工业大学）；Helmholtz Center Munich（亥姆霍兹慕尼黑中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；Aalto University（阿尔托大学）
