...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of cognitive engineering and decision making >Teaching Machines to Recognize Neurodynamic Correlates of Team and Team Member Uncertainty
【24h】

Teaching Machines to Recognize Neurodynamic Correlates of Team and Team Member Uncertainty

机译：识别团队和团队成员不确定性神经动力相关性的教学机

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We describe efforts to make humans more transparent to machines by focusing on uncertainty, a concept with roots in neuronal populations that scales through social interactions. To be effective team partners, machines will need to learn why uncertainty happens, how it happens, how long it will last, and possible mitigations the machine can supply. Electroencephalography-derived measures of team neurodynamic organization were used to identify times of uncertainty in military, health care, and high school problem-solving teams. A set of neurodynamic sequences was assembled that differed in the magnitudes and durations of uncertainty with the goal of training machines to detect the onset of prolonged periods of high level uncertainty, that is, when a team might require support. Variations in uncertainty onset were identified by classifying the first 70 s of the exemplars using self-organizing maps (SOM), a machine architecture that develops a topology during training that separates closely related from desperate data. Clusters developed during training that distinguished patterns of no uncertainty, low-level and quickly resolved uncertainty, and prolonged high-level uncertainty, creating opportunities for neurodynamic-based systems that can interpret the ebbs and flows in team uncertainty and provide recommendations to the trainer or team in near real time when needed.
机译：我们描述了通过专注于不确定性来使人类对机器更加透明的努力，不确定性是起源于神经元种群的概念，该概念通过社交互动而扩展。为了成为有效的团队合作伙伴，机器将需要学习不确定性为什么发生，如何发生，持续多长时间以及机器可以提供的缓解措施。脑电图得出的团队神经动力组织测量值用于确定军事，卫生保健和高中解决问题团队的不确定时期。组装了一组神经动力学序列，其不确定性的大小和持续时间各不相同，其目的是训练机器来检测高水平不确定性的延长周期的开始，也就是说，当团队可能需要支持时。通过使用自组织映射（SOM）对样本的前70 s分类来确定不确定性发作的差异，SOM是一种机器结构，在训练过程中会形成拓扑结构，将拓扑结构与绝望的数据密切相关。在训练过程中形成的集群可以区分无不确定性，低水平和快速解决的不确定性以及长时间的高水平不确定性，从而为基于神经动力学的系统创造了机会，这些系统可以解释团队不确定性的起伏和向教练员或教练提供建议需要时，团队几乎实时。

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