Advancing Theory and Trust in Cognitive Systems

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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.