The integration of computational and physical processes is central to the functioning of Cyber-Physical Systems (CPS). As these systems become more complex, they require advanced algorithms to enable efficient communication, seamless data processing and secure, real-time decision-making. Yet, despite the rapid growth in artificial intelligence technologies, we're still far from harnessing the true potential of these systems. Current AI models often lack the capacity for learning, adaptation, and interaction in environments that are dynamic, uncertain and may pose risk. While human intervention and decision-making are essential in operating CPS, the challenge lies in building intelligent machines capable of mimicking human reasoning in these contexts.
Bringing together our interdisciplinary expertise in artificial intelligence, robotics, information theory, and cognitive science, we aim to devise Human-Machine Intelligence frameworks for CPS. In these systems, a machine learning model, akin to the human brain, communicates with sensors and actuators, simulating a biological nervous system. This hybrid model would leverage advanced machine learning algorithms to learn from its environment, react to changes, and make decisions in real-time, similar to how humans navigate dynamic scenarios. To facilitate this, we propose to build a machine learning model that employs cognitive computing, allowing the system to mimic human thought processes. This involves designing algorithms that can interpret unstructured data, understand context, learn from experiences and interactions, reason through problems, and adapt to changing environments.