Plan Assessment for Autonomous Manufacturing as Bayesian Inference

机译：贝叶斯推理的自动制造计划评估

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Next-generation autonomous manufacturing plants create individualized products by automatically deriving manufacturing schedules from design specifications. However, because planning and scheduling are computationally hard, they must typically be done offline using a simplified system model, meaning that online observations and potential component faults cannot be considered. This leads to the problem of plan assessment: Given behavior models and current observations of the plant's (possibly faulty) behavior, what is the probability of a partially executed manufacturing plan succeeding? In this work, we propose 1) a statistical relational behavior model for a class of manufacturing scenarios and 2) a method to derive statistical bounds on plan success probabilities for each product from confidence intervals based on sampled system behaviors. Experimental results are presented for three hypothetical yet realistic manufacturing scenarios.

机译：下一代自主制造工厂通过自动从设计规格中得出制造计划来创建个性化产品。但是，由于计划和调度在计算上比较困难，因此通常必须使用简化的系统模型在离线状态下进行，这意味着无法考虑在线观察和潜在的组件故障。这就导致了计划评估的问题：给定行为模型和当前工厂（可能有故障）行为的观察结果，部分执行的制造计划成功的可能性是多少？在这项工作中，我们提出1）一类制造场景的统计关系行为模型，以及2）一种基于抽样系统行为从置信区间得出每种产品的计划成功概率的统计范围的方法。给出了三种假设但现实的制造方案的实验结果。

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《KI 2010: Advances in artificial intelligence》|2010年|p.263-271|共9页
会议地点 Karlsruhe(DE);Karlsruhe(DE)
作者
Paul Maier; Dominik Jain; Stefan Waldherr; Martin Sachenbacher;
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Technische Universitat Miinchen, Department of Informatics BoltzmanstraBe 3, 85748 Garching, Germany;

Technische Universitat Miinchen, Department of Informatics BoltzmanstraBe 3, 85748 Garching, Germany;

Technische Universitat Miinchen, Department of Informatics BoltzmanstraBe 3, 85748 Garching, Germany;

Technische Universitat Miinchen, Department of Informatics BoltzmanstraBe 3, 85748 Garching, Germany;

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正文语种 eng
中图分类人工智能理论;
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Plan Assessment for Autonomous Manufacturing as Bayesian Inference

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