公开(公告)号：US20150046388A1

公开(公告)日：2015-02-12

申请号：US14453261

申请日：2014-08-06

申请人： Amit P. Sheth ,  Cory A. Henson ,  Krishnaprasad Thirunarayan

发明人： Amit P. Sheth ,  Cory A. Henson ,  Krishnaprasad Thirunarayan

IPC分类号： G06N5/02

CPC分类号： G06N5/022

摘要： Machine semantic perception is discussed. One example system can comprise an environmental knowledgebase (KB) associating features with properties, and an interface component receiving sensor data associated with observed properties. The KB and sensor observations can be encoded in bit-matrix and bit-vector representations, respectively, for efficient storage and computation. A perception component can perform semantic perception on observed properties based on the KB. The perception component can determine explanatory features associated with the observed properties through abductive reasoning, and determine discriminatory properties associated with the explanatory features through deductive reasoning. These can be executed in an iterative and interleaved Perception Cycle for efficient computation of minimum actionable explanations of observations. The bit-matrix and bit-vector representations are presented for efficient computation of minimum actionable explanation using perception cycle, the iterative and interleaved application of hybrid abductive and deductive reasoning to seek contextually relevant discriminatory observations to systematically narrow explanatory features.

摘要翻译： 讨论机器语义知觉。 一个示例性系统可以包括将特征与属性相关联的环境知识库（KB），以及接收与观察到的属性相关联的传感器数据的接口部件。 KB和传感器观测值可以分别编码在位矩阵和位矢量表示中，以实现有效的存储和计算。 感知组件可以基于KB执行观察属性的语义感知。 感知组件可以通过诱导推理来确定与观察属性相关的说明性特征，并通过演绎推理确定与解释特征相关的辨别性质。 这些可以在迭代和交织的感知循环中执行，以便有效地计算观察值的最小可行解释。 呈现位矩阵和位向量表示，用于有效计算使用感知周期的最小可行解释，迭代和交织应用混合引导和演绎推理，以寻求与情境相关的歧视性观察来系统地缩小说明特征。