Explainable AI in Internet of Control System Distributed at Edge-Cloud Architecture

Many current control systems are restricted to highly controlled environments. In complicated dynamic and unstructured environments such as autonomous vehicles, control systems must be able to deal with more and more complex state situations. In complex systems with large number of states, it is often too slow to use optimal planners and developing heuristic tactics for high level goals can be challenging. AI control is an attractive alternative to traditional control architectures due to their capability to approximate optimal solutions in high dimensional state spaces without requiring a human-designed heuristic. Explainable AI control attempts to produce a human readable control command which is both interpretable and manipulable. This paper is an attempt to propose an architecture for explainable AI control in edge-cloud environment in which there are connected autonomous agents that need to be controlled. In this architecture the designed controller is distributed across the edge and cloud platform using explainable AI. This architecture could be introduced as Internet of Control Systems (IoCS), which could be applied as distributed tactics to control of connected autonomous agents. The IoCS attempts to unleash AI services using resources at the edge near the autonomous agents and make intelligent edge for dynamic, adaptive, and optimized AI control.