论文标题
基于知识的实体预测自主系统中改进的机器感知
Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems
论文作者
论文摘要
基于知识的实体预测（KEP）是一项新任务，旨在改善自主系统中的机器感知。 KEP在预测潜在的未识别实体方面利用了异质来源的关系知识。在本文中，我们将KEP的正式定义作为知识完成任务。然后引入了三种潜在的解决方案，这些解决方案采用了几种机器学习和数据挖掘技术。最后，在来自不同域的两个自主系统上证明了KEP的适用性。即自动驾驶和智能制造。我们认为，在复杂的现实世界系统中，使用KEP将显着改善机器感知，同时将当前技术推向实现完全自治的一步。
Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems. KEP leverages relational knowledge from heterogeneous sources in predicting potentially unrecognized entities. In this paper, we provide a formal definition of KEP as a knowledge completion task. Three potential solutions are then introduced, which employ several machine learning and data mining techniques. Finally, the applicability of KEP is demonstrated on two autonomous systems from different domains; namely, autonomous driving and smart manufacturing. We argue that in complex real-world systems, the use of KEP would significantly improve machine perception while pushing the current technology one step closer to achieving full autonomy.
