Advancing Theory and Trust in Cognitive Systems

推进认知系统的理论和信任

• 批准号: RGPIN-2022-04853
• 负责人: Gadsden, Stephen
• 金额: $ 2.33万
• 依托单位: McMaster University
• 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=753181
• 关键词: Advancing; Theory; Trust; Cognitive; Systems

Smart systems are found everywhere in our increasingly automated and interconnected world-from smart homes to self-driving vehicles. The next-generation smart system, known as a cognitive system, is a type of system that operates in an environment with a perceived amount of cognition. These are smart systems with a higher-level of intelligence that behave autonomously. Fundamentally, the safe and reliable operation of a cognitive system is heavily dependent on its sensors, its understanding of the collected data, and its interaction with the environment. Based on the sensor data, a cognitive system may build its knowledge base and models using machine learning techniques. However, this generally creates models that are input-output driven with minimal information or user understanding on the dynamics of the system. This raises an important question on the future of cognitive systems. Can we rely solely on a cognitive system for critical or unsafe tasks, or should they be regulated to passive actions and menial duties? I propose a research program that, in the next five years, will focus on novel and efficient ways of advancing the theory of cognitive systems while improving public confidence, acceptance, and trust in their application. These activities will support the long-term goal of my research program which is making cognitive systems more reliable and accessible. Three main activities will support this program. Develop robust estimation and control strategies to improve cognitive system performance within the perception-action cycle. We will compare and develop robust estimators and controllers to: 1) extract as much useful information from the environment as possible; 2) allow for reliable datasets to create our system's knowledge base; 3) enable safe and predictable control actions; and, 4) improve overall system reliability and performance. Fuse machine learning and physics-based modelling for improved system intelligence. We will investigate methods to fuse the two types of models in an effort to: 1) improve the optimization of the decision-making process; 2) increase overall system performance; and, 3) help the engineer or user better understand the reasoning or actions of the cognitive system. Provide the ability for a cognitive system to explain and reason its actions to improve user trust. We will investigate and develop confidence factors and explainability to: 1) describe how and why a particular solution was obtained, in plain language, to the user; and, 2) increase user trust and acceptance of the cognitive system. Through the activities supporting these aims, my research program will develop more reliable and safe cognitive systems that will improve the daily lives of humans.

在我们日益自动化和互联的世界里，智能系统随处可见--从智能家居到自动驾驶汽车。下一代智能系统被称为认知系统，是一种在感知到的认知量的环境中运行的系统。这些智能系统具有更高水平的智能，可以自主运行。从根本上说，认知系统的安全可靠运行在很大程度上依赖于它的传感器，它对收集的数据的理解，以及它与环境的互动。基于传感器数据，认知系统可以使用机器学习技术来建立其知识库和模型。然而，这通常创建的模型是输入-输出驱动的，只需要最少的信息或用户对系统动态的理解。这对认知系统的未来提出了一个重要的问题。对于关键或不安全的任务，我们能仅仅依靠认知系统吗？还是应该把它们限制为被动的行动和卑微的职责？我提出了一个研究计划，在接下来的五年里，将专注于以新颖而有效的方式推进认知系统理论，同时提高公众对其应用的信心、接受度和信任度。这些活动将支持我的研究计划的长期目标，即使认知系统更可靠和更容易获得。三项主要活动将支持这一计划。制定稳健的估计和控制策略，以改善认知系统在感知-行动周期内的性能。我们将比较和开发稳健的估计器和控制器，以：1)从环境中提取尽可能多的有用信息；2)允许可靠的数据集创建我们系统的知识库；3)实现安全和可预测的控制行动；以及4)提高整体系统的可靠性和性能。融合机器学习和基于物理的建模，以改进系统智能。我们将研究融合这两种模型的方法，以努力：1)改进决策过程的优化；2)提高系统的整体性能；3)帮助工程师或用户更好地理解认知系统的推理或行为。为认知系统提供解释和推理其行为的能力，以提高用户信任。我们将调查和开发信心因素和可解释性：1)用通俗易懂的语言描述如何以及为什么获得特定的解决方案；以及，2)增加用户对认知系统的信任和接受度。通过支持这些目标的活动，我的研究计划将开发出更可靠、更安全的认知系统，从而改善人类的日常生活。