  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.
