• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Symposium on Software Engineering for Adaptive and Self-Managing Systems >Using Unstructured Data to Improve the Continuous Planning of Critical Processes Involving Humans
【24h】

Using Unstructured Data to Improve the Continuous Planning of Critical Processes Involving Humans

机译：使用非结构化数据来改善涉及人类的关键过程的持续规划

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The success of processes executed in uncertain and changing environments is reliant on the dependable use of relevant information to support continuous planning at runtime. At the core of this planning is a model which, if incorrect, can lead to failures and, in critical processes such as evacuation and disaster relief operations, to harm to humans. Obtaining reliable and timely estimations of model parameters is often difficult, and considerable research effort has been expended to derive methods for updating models at run-time. Typically, these methods use data sources such as system logs, run-time events and sensor readings, which are well structured. However, in many critical processes, the most relevant data are produced by human participants to, and observers of, the process and its environment (e.g., through social media) and is unstructured. For such scenarios we propose COPE, a work-in-progress method for the continuous planning of critical processes involving humans and carried out in uncertain, changing environments. COPE uses a combination of runtime natural-language processing (to update a stochastic model of the target process based on unstructured data) and stochastic model synthesis (to generate Pareto-optimal plans for the process). Preliminary experiments indicate that COPE can support continuous planning effectively for a simulated evacuation operation after a natural disaster.
机译：在不确定和更改环境中执行的进程的成功依赖于相关信息的可靠性，以支持运行时的连续规划。在这个规划的核心，是一个模型，如果不正确，可以导致失败，并且在疏散和救灾行动等关键过程中，伤害人类。获得可靠和及时的模型参数估计通常困难，并且已经消耗了大量的研究工作来推导在运行时更新模型的方法。通常，这些方法使用诸如系统日志，运行时事件和传感器读数的数据源，这是结构良好的。然而，在许多关键过程中，最相关的数据由人类参与者和观察者的过程和环境（例如，通过社交媒体）产生，并且是非结构化的。对于这种情况，我们提出了应对，持续规划涉及人类的关键过程的过程中的过程中，并在不确定的环境中进行。 COPE使用运行时自然语言处理的组合（基于非结构化数据更新目标过程的随机模型）和随机模型合成（为该过程产生帕累托最优计划）。初步实验表明，应对在自然灾害后的模拟疏散操作有效地支持连续规划。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号