In this paper, we present methods for two types of metacognitive tasks in an AI system: rapidly expanding a neural classification model to accommodate a new category of object, and recognizing when a novel object type is observed instead of misclassifying the observation as a known class. Our methods take numerical data drawn from an embodied simulation environment, which describes the motion and properties of objects when interacted with, and we demonstrate that this type of representation is important for the success of novel type detection. We present a suite of experiments in rapidly accommodating the introduction of new categories and concepts and in novel type detection, and an architecture to integrate the two in an interactive system.

翻译：在本文中,我们提出了在AI系统中开展两类元化任务的方法:迅速扩展神经分类模式以适应新的物体类别,在观测到新物体类型而不是将观测误分类为已知类别时确认新物体类型。我们的方法取自一个内含模拟环境的数值数据,该模拟环境描述与物体互动时的动作和特性,我们证明这种类型的表示方式对于新类型探测的成功非常重要。我们提出了一套迅速适应引进新类别和概念以及新类型探测的实验,以及一种将两者纳入互动系统的结构。