Abstract: The techniques disclosed herein enable systems to enhance autonomous process control platforms using a quality aware machine learning agent. To achieve this, a machine learning agent is integrated into a process control system. The machine learning agent extracts a set of states from an environment containing the process and defines a set of corresponding quality states which are then extracted from the environment as well. Based on the set of states and quality states, the machine learning agent determines a set of actions that modify operating parameters of the process. Applying the actions results in an updated set of states and quality states which can be analyzed to compute an optimality score, quantifying the effectiveness of the actions. Based on the updated states and quality states, the machine learning agent determines a modified set of actions to apply to the environment and increase the optimality score.

Abstract: The techniques disclosed herein enable systems to solve routing problems using machine learning augmented by optimization modules. To plot a route, a system receives a plurality of nodes from a problem space. The plurality of nodes is then analyzed by an optimization module and ranked based on various criteria such as distance from a reference node and deadline. Based on the ranking, the optimization module can select a smaller subset of nodes that is then processed by a machine learning model. The machine learning model can then select a node from the subset of nodes for addition to a route. This process can be repeated until a route is plotted for the full set of nodes within the problem space. In addition, the system can be configured to monitor current conditions of the problem space to modify the route in response to changes.

Abstract: Methods, systems, and computer programs are presented for scheduling resources used for package delivery. One method includes an operation for initializing a reinforcement learning (RL) agent that calculates staff requirements for performing jobs, each job including a delivery of a package to a respective location. The method further includes training the RL agent by performing a set of iterations. Each iteration includes operations for accessing job data, the job data including jobs for delivery, coordinates for the deliveries, and deadlines for the deliveries; generating clusters in a map for the jobs using unsupervised learning; generating the staff requirements by the RL agent based on feature extraction from the spatial and temporal distribution of the jobs; calculating a reward for the generated staff requirements; and modifying the RL agent using reinforcement learning based on the reward. Further, the trained RL agent is utilized for determining staff requirements for new jobs.

Abstract: Systems and methods related to autonomous control of supervisory setpoints using artificial intelligence are described. In one example, a method including using a measurable attribute associated with a system, segmenting operational data associated with the system into at least a first bin and a second bin, is provided. The method further includes training a first brain based on a first data model associated with the first bin and training a second brain based on a second data model associated with the second bin. The method further includes using the first brain and the second brain, implemented by at least one processor, automatically generating predicted supervisory control suggestions for a plurality of supervisory setpoints associated with the system.