论文标题
在体现的模拟环境中检测和适应新型类型和概念
Detecting and Accommodating Novel Types and Concepts in an Embodied Simulation Environment
论文作者
论文摘要
在本文中，我们介绍了AI系统中两种类型的元认知任务的方法：快速扩展神经分类模型以适应新的对象类别，并认识到何时观察到新颖的对象类型，而不是将观察结果误解为已知类别。我们的方法采用从体现的模拟环境中绘制的数值数据，该数据描述了与对象进行交互时的运动和特性，我们证明了这种类型的表示对于新型类型检测的成功很重要。我们提出了一套实验，以迅速适应新类别和概念以及新型类型检测的引入，以及将两者集成到交互式系统中的体系结构。
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.
