Human-AI interactions in real-world complex uncertain environments using a comprehensive reinforcement learning framework

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The advent of Artificial Intelligence (AI) as a core technological approach to solve specific problems is both undeniable and remarkable. However, AI still operates primarily as a tool to execute narrow-focus tasks, rather than a supporting partner in a relationship with human users. Considering human and AI respective strengths and weaknesses, such a man-machine partnership has the potential to become more than the sum of its parts, leveraging complementary abilities to achieve results that would be otherwise impossible or very difficult to achieve with only one or the other. However, for AI agents to work as synergistically as possible with human users/operators, specific methods, approaches and technologies are warranted. The object of the proposed research is to advance those technologies and approaches, as well as to demonstrate their benefits in a real-life setting through practical application in a complex and sensitive environment. This project will combine the efforts of researchers from the University of Alberta and the JACOBB center with resources provided by AIR and Thales. Within this collaboration, we will investigate how human knowledge can be used to train AI agents in complex and real environments. It will also allow us to understand where and when the interaction of agents and humans allows us to achieve higher performance than agents alone or humans alone. To achieve this, different frameworks, interfaces and types of collaboration will first be tested. Subsequently, the feedback systems will be evaluated and compared with each other, sometimes involving humans and AI agents working alone, and sometimes a hybrid approach. The expected results will make the COGMENT platform, developed by AIR and at the heart of the methodology used for this project, more widely accessible and usable by the community.