当前位置: X-MOL 学术Artif. Intell. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Quantifying Controllability in Temporal Networks with Uncertainty
Artificial Intelligence ( IF 5.1 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.artint.2020.103384
Shyan Akmal , Savana Ammons , Hemeng Li , Michael Gao , Lindsay Popowski , James C. Boerkoel

Abstract Controllability for Simple Temporal Networks with Uncertainty (STNUs) has thus far been limited to three levels: strong, dynamic, and weak. Because of this, there is currently no systematic way for an agent to assess just how far from being controllable an uncontrollable STNU is. We provide new insights inspired by a geometric interpretation of STNUs to introduce the degrees of strong and dynamic controllability — continuous metrics that measure how far a network is from being controllable. We utilize these metrics to approximate the probabilities that an STNU can be dispatched successfully offline and online respectively. We introduce new methods for predicting the degrees of strong and dynamic controllability for uncontrollable networks. We further generalize these metrics by defining likelihood of controllability, a controllability measure that applies to Probabilistic Simple Temporal Networks (PSTNs). Finally, we empirically demonstrate that these metrics are good predictors of actual dispatch success rate for STNUs and PSTNs.

中文翻译:

量化具有不确定性的时间网络的可控性

摘要 具有不确定性的简单时间网络 (STNU) 的可控性迄今为止仅限于三个级别:强、动态和弱。正因为如此,目前还没有系统的方法来评估一个不可控的 STNU 离可控有多远。我们提供了受 STNU 几何解释启发的新见解,以介绍强和动态可控性的程度——衡量网络离可控有多远的连续指标。我们利用这些指标来近似估计 STNU 可以分别在线和离线成功调度的概率。我们引入了预测不可控网络的强和动态可控程度的新方法。我们通过定义可控性的可能性来进一步概括这些指标,适用于概率简单时间网络 (PSTN) 的可控性度量。最后,我们凭经验证明这些指标是 STNU 和 PSTN 实际调度成功率的良好预测指标。
更新日期:2020-12-01
down
wechat
bug