Ontologies for the Physical Turing Test

图灵测试最初是作为一种基于自然语言对话来评估软件系统如何很好地代表人类智能的方法而提出的。最近人们认识到，需要一种不同的方法，将通用智能所需的能力结合起来。视觉图灵测试将图灵测试扩展到包括感知——它指定了一个基于描绘现实世界场景的图像语料库的问题回答的开放域任务。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。为了获取执行物理图灵测试所需的常识性知识，计算机视觉和机器人技术的现有方法已经开始包括简单的知识表示方案，如WordNet。然而，这种方法不能完全指定术语的预期语义，也不能支持自动推理。需要的是本体，即计算机可解释的规范，代理、应用程序或其他信息资源使用它来声明它使用的术语以及这些术语的含义。在这个项目中，我们将设计一阶逻辑的新本体，可以表示物理世界中的物体，这些物体可能参与的活动，以及如何识别这些物体以及它们之间在图像中的空间关系。（我们将把这些本体称为PRAxIS本体，用于智能系统的感知、推理和行动）这些本体将被集成以支持对物理对象、它们的行为（例如它们可以改变的方式）和它们的交互（例如对固体物理对象的操作）的推理。感知本体将使用基于真实世界图像的问答进行测试，例如检测和定位对象实例以及场景中对象之间的关系。行动本体将通过表示组装、使用和维护物理对象（包括设备和家具等日常物品）的指令来进行测试。为了支持物理图灵测试的问答，该项目还将研究一阶本体的推理（定理证明和约束满足）。

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.