SGER: Concepts in Context: Representations for Recognition

这个项目旨在研究如何定义和表示概念的基本问题，特别是在机器人需要识别和使用对象的情况下。这个项目的重点是从观察对象使用情况中得到的表示。基本的假设是，使用揭示了关于对象性质的基本信息。本项目要解决的基本问题是：如何根据多重表征来定义概念，以便有效识别？这个项目将研究如何上下文和用法可以允许识别，当一个对象被孤立地观察时，可能是不可能的，以及如何通过功能定义和简单的视觉描述的组合来解释对象的视觉外观的巨大变化。该项目围绕三个任务组织：（1）调查多方面的知识表示技术，包括基于范例和基于使用的表示，以及使用加权（2）允许证据和加权表示的概率平衡以及基于范例和非基于范例的表示的组合的多表示识别算法;以及（3）参数和结构学习，其允许在概念表示和定义中使用的权重的自适应。该项目将使用配备各种传感器的移动的机器人作为试验台。除了自己的感官输入外，机器人还将使用人类与环境中物体互动的观察结果。该项目将建立在PI正在进行的关于使用视觉特征和功能表示的对象定义和识别技术的工作基础上。该项目不仅对机器人技术和人工智能，而且对认知科学和心理学都有潜在的更广泛的影响。

ABSTRACTThis project seeks to investigate fundamental questions about how to define and represent concepts, particularly in contexts where robots need to identify and use objects. This project focuses on representations that result from observations about object usage. The underlying assumption is that usage reveals fundamental information about the nature of objects. The fundamental question to be addressed in this project is: How can concepts be defined in terms of multiple representations for the purpose of effective recognition? This project will investigate how context and usage can allow recognition that might not be possible when an object is observed in isolation, and how the enormous variation in the visual appearance of objects can be accounted for through combination of functional definitions and simple visual descriptions. The project is organized around three tasks: (1) investigation of multi-faceted knowledge representation techniques that include exemplar-based and usage-based representations, and the use of weighted (probabilistic) representations; (2) multi-representation recognition algorithms that allow for probabilistic balancing of evidence and weighted representations and the combining of exemplar-based and non-exemplar-based representations; and (3) parameter and structure learning that allows adaptation of weights used in concept representations and definitions. The project will use mobile robots equipped with various sensors as a test bed. In addition to its own sensory input, the robot will use observations of humans interacting with objects in its environment. The project will build on the PIs ongoing work on object definition and recognition techniques that use visual features and functional representations. This project has potential Broader Impact not only on robotics and artificial intelligence but also on cognitive science and psychology in general.