Ontologies for the Physical Turing Test

物理图灵测试本体

• 批准号: RGPIN-2020-05781
• 负责人: Gruninger, Michael
• 金额: $ 2.99万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759653
• 关键词: Ontologies; Physical; Turing; Test

The Turing Test was originally proposed as a way of evaluating how well a software system represents human intelligence, based on natural language conversations.  It has recently been recognized that a different approach is needed that incorporates capabilities required for general intelligence. The Visual Turing Test extends the Turing Test to include perception -- it specifies an open domain task of question answering based on corpora of images depicting real-world scenes.  The Physical Turing Test extends this further by specifying questions that require the integration of perception, reasoning, and action. To capture the commonsense knowledge required to perform the Physical Turing Test, existing approaches to computer vision and robotics have begun to include simple knowledge representation schemes such as WordNet.  However, such approaches cannot fully specify the intended semantics of the terminology,nor can they support automated reasoning.  What is needed is an ontology, that is, a computer interpretable specification that is used by an agent, application, or other information resource to declare what terms it uses, and what the terms mean. In this project, we will design new ontologies in first-order logic that can represent objects in the physical world, the activities in which these objects can possibly participate, and how to recognize these objects and the spatial relationships between them within images. (We will refer to these as the PRAxIS Ontologies, for Perception, Reasoning, and Action across Intelligent Systems) These ontologies will be integrated to support reasoning about physical objects, their behaviours (e.g. the ways in which they can change), and their interactions (e.g.manipulation of solid physical objects). Ontologies for perception will be tested using question-answering based on real-world images, such as detecting and localizing instances of objects and relationships among the objects in a scene. Ontologies for action will be tested through the representation of instructions for assembling, using, and maintaining physical objects, including devices and everyday objects such as furniture To support the question-answering for the Physical Turing Test, this project will also investigate reasoning (theorem proving and constraint satisfaction) with first-order ontologies.

Turing测试最初是作为评估软件系统根据自然语言对话来代表人类智能的一种方式的一种方式。最近已经认识到，需要另一种方法来结合通用情报所需的功能。 Visual Turing测试将图灵测试扩展到包括感知 - 它根据描述现实世界场景的图像的图像指定了一个开放的域任务。物理图灵测试通过指定需要集成感知，推理和行动的问题进一步扩展了这一点。为了捕获执行物理图灵测试所需的常识知识，现有的计算机视觉和机器人技术方法已经开始包括简单的知识表示方案，例如WordNet。但是，这种方法无法完全指定术语的预期语义，也无法支持自动推理。所需的是一个本体，即代理，应用程序或其他信息资源使用的计算机可解释的规范来声明其使用的条款以及术语的含义。在这个项目中，我们将设计一阶逻辑的新本体，这些本体可以代表物理世界中的对象，这些对象可能参与的活动以及如何识别这些对象以及图像中它们之间的空间关系。 （我们将它们称为实践本体，以跨智能系统的感知，推理和行动）这些本体将集成以支持有关物理对象，其行为的推理，其行为（例如它们可以改变的方式）及其相互作用（例如，固体物理对象的操作）。感知的本体将使用基于现实世界图像的问题进行检索，例如检测和本地化对象和对象之间的关系的本地化实例。将通过代表组装，使用和维护物理对象（包括设备和每天的对象）（例如家具）来支持的，以支持物理图灵测试的问题，该项目还将研究推理（定理提供和约束满意度），以一阶的本体本体研究，将对行动的本体进行测试。