From Perception and Learning to Understanding and Action

从感知和学习到理解和行动

• 批准号: RGPIN-2020-06837
• 负责人: Pal, Christopher
• 金额: $ 3.5万
• 依托单位: École Polytechnique de Montréal
• 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=754210
• 关键词: Perception; Learning; Understanding; Action

Artificial Intelligence (AI) has long concerned itself with the notion of creating intelligent agents capable of observing the world and selecting actions to take based on those observations. The proposed research program will be pursued through exploring fundamental research problems linked to the combination of computer vision, embodied intelligence, natural language understanding, deep learning, reinforcement learning and robotics. The research program will make extensive use of simulation environments and human--agent interaction to explore deep learning, deep reinforcement learning, and question answering with embodied agents. Research will explore fundamental questions as well as realistic settings including: autonomous vehicles, robots that physically interact with the world and purely software based information agents. This research will examine three representative settings. (1) Autonomous vehicles: in this setting, many of the abstract concepts discussed above become more firmly grounded in a concrete task. Furthermore, key problems associated with many sub-disciplines of AI such as computer vision, automated reasoning, planning and the paradigm of reinforcement learning (RL) can become clearer when grounded in such an application. (2) Other settings such as those associated with the creation of more general-purpose robotics for industrial and home settings can also serve as grounding applications. In these settings an agent's understanding of how the world and objects within the world physically interact with the agent also becomes an important element of the problem. As one begins to envision the way in which people might interact with more general purpose and "embodied" intelligent agents, natural language emerges as a powerful way to facilitate human--robot interactions. Language can also serve as a mechanism for teaching agents new capabilities. Language can also be coupled with visual demonstrations to further enhance the learning of an embodied agent. (3) Intelligent assistants - such as those that interact with the user through a smartphone or smart speaker, could be thought of as software agents that exist in a world of information. The combination of physically embodied agents with information agents could propel the benefits of AI into the everyday world of Canadians. The large scale testing of autonomous cars is underway in North America and cars without safety drivers are now operating in the United States. The autonomous car oriented research proposed here explores subjects of critical importance to Canada and Canadians in terms of both public safety and economic prosperity. Further, more general-purpose robotics has the potential to transform manufacturing, distribution and delivery services and well as provide powerful assistance tools for the disabled.

人工智能（AI）长期以来一直关注创造能够观察世界并根据这些观察选择行动的智能代理的概念。拟议的研究计划将通过探索与计算机视觉，体现智能，自然语言理解，深度学习，强化学习和机器人技术相结合的基础研究问题来进行。该研究计划将广泛利用模拟环境和人机交互来探索深度学习，深度强化学习和带有具体代理的问题回答。研究将探索基本问题以及现实环境，包括：自动驾驶汽车，与世界进行物理交互的机器人和纯粹基于软件的信息代理。 本研究将探讨三种具有代表性的设置。(1)自动驾驶汽车：在这种情况下，上面讨论的许多抽象概念变得更加牢固地扎根于具体任务。此外，与人工智能的许多子学科相关的关键问题，如计算机视觉、自动推理、规划和强化学习（RL）范式，在这种应用中可以变得更加清晰。(2)其他设置，例如与为工业和家庭环境创建更通用的机器人相关的设置，也可以作为接地应用。在这些设置中，代理人对世界和世界中的对象如何与代理人物理交互的理解也成为问题的重要因素。当人们开始设想人们与更通用和“具体化”的智能代理交互的方式时，自然语言成为促进人类-机器人交互的强大方式。语言也可以作为一种机制，教导代理人新的能力。语言也可以与视觉演示相结合，以进一步增强具体代理的学习。(3)智能助理-例如通过智能手机或智能扬声器与用户交互的智能助理，可以被认为是存在于信息世界中的软件代理。物理实体代理与信息代理的结合可以将人工智能的好处推向加拿大人的日常世界。 北美正在进行大规模自动驾驶汽车测试，没有安全驾驶员的汽车目前正在美国运营。这里提出的自动驾驶汽车导向研究探讨了对加拿大和加拿大人在公共安全和经济繁荣方面至关重要的主题。此外，更多的通用机器人有可能改变制造、分销和交付服务，并为残疾人提供强大的辅助工具。