Assistive sequential decision making framework

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This research focuses on developing algorithms to maximize the impact of smart technologies when they are integrated into business processes. We will specifically investigate team decision making problems, in which only a sub-set of decision makers are capable of making decisions optimally to achieve the overarching goal of the entire team. The agents either optimal or sub-optimal make decisions over time as the system makes transitions through states. Upon reaching a decision, each agent will receive a reward conditional on the system state and actions taken by her as well as all other agents.******The proposed decision process is useful in a wide range of applications, from manufacturing to supply chain management to military operations. For instance, the decision process could maximize the productivity of a robotic assembly cell where two robots and a human operator work together to assemble a large automotive component. Rather than separating robots from human operators for safety, the proposed decision processes could help ensure the robots can plan their paths of movement so that they complement the operations done by the human operators while ensuring the safety of the human operators.******The proposed assistive decision process is an extension of the Markov decision processes to multi-agent scenarios as well as reinforcement learning. The presence of multiple decision makers poses challenges such as computational complexity, stability of the process and behavioural modeling of human decision makers. These challenges have resulted in the current literature severely lacking in this area despite the increasing importance of the topic as the society turns more and more automated. This program will tackle these three challenges using dimension reduction, optimal trade-offs between exploration and exploitation, and inverse reinforcement learning. ******This research is timely given the rapid adoption of artificial intelligence and autonomous systems. The assistive decision process can be used as the mathematical backbone in control systems to overcome the challenges that currently limit the use of autonomous multi-agent systems. This work will thus help Canada become a leader in autonomous manufacturing, transportation, surveillance and security, and communications. **