Abstract: Reinforcement learning may be used to train machine learning (ML) models for robotic process automation (RPA) that are implemented by robots. A policy network may be employed, which learns to achieve a definite output by providing a particular input. In other words, the policy network informs the system whether it is getting closer to the winning state. The policy network may be refined by the robots automatically or with the periodic assistance of a human in order to reach the winning state, or to achieve a more optimal winning state. Robots may also create other robots that utilize reinforcement learning.

Abstract: Using long-running workflows with artificial intelligence flows to manage the training/retraining lifecycle of artificial intelligence (AI)/machine learning (ML) models is disclosed. Validation may be desired when an AI/ML model is called by a robotic process automation (RPA) robot executing the long-running workflow. This validation includes dynamic input from users. The RPA robot receives the dynamic input from the users and uses this data for training a replacement AI/ML model or retraining the called AI/ML model. The state of the long-running workflow may be preserved, both in training and serving. Long-running workflows may be used to keep track of where the current execution is in the ML model lifecycle.

Abstract: Artificial intelligence (AI)/machine learning (ML) model drift detection and correction for robotic process automation (RPA) is disclosed. Information is analyzed pertaining to input data for an AI/ML model to determine whether data drift has occurred, analyze information pertaining to results from execution of the AI/ML model to determine whether model drift has occurred, or both. When, based on the analysis of the information, a change condition is found, a change threshold is met or exceeded, or both, the AI/ML model is retrained. The retrained AI/ML model may then be deployed to provide better predictions on real world data.

Abstract: A machine learning (ML) model retraining pipeline for robotic process automation (RPA) is disclosed. When an ML model is deployed in a production or development environment, RPA robots send requests to the ML model when executing their workflows. When a confidence level of the ML model falls below a certain confidence, training data is collected, potentially from a large number of computing systems. The ML model is then trained using at least in part the collected training data, and a new version of the ML model is deployed.

Abstract: Reinforcement learning may be used to train machine learning (ML) models for robotic process automation (RPA) that are implemented by robots. A policy network may be employed, which learns to achieve a definite output by providing a particular input. In other words, the policy network informs the system whether it is getting closer to the winning state. The policy network may be refined by the robots automatically or with the periodic assistance of a human in order to reach the winning state, or to achieve a more optimal winning state. Robots may also create other robots that utilize reinforcement learning.

Abstract: According to examples, network sampling based path decomposition and anomaly detection may include evaluating computer-generated log file data to generate a master network graph that specifies known events and transitions between the known events, and decomposing the master network graph to generate a representative network graph that includes a reduced number of paths of the master network graph. A source may be monitored to determine a cyber security threat by receiving incoming log file data related to the source, comparing the incoming log file data related to the source to the representative network graph, and determining, based on the comparison of the incoming log file data related to the source to the representative network graph, an anomaly in the representative network graph. Further, based on the monitoring, a report indicative of the cyber security threat may be generated based on the anomaly in the representative network graph.

Abstract: According to examples, network sampling based path decomposition and anomaly detection may include evaluating computer-generated log file data to generate a master network graph that specifies known events and transitions between the known events, and decomposing the master network graph to generate a representative network graph that includes a reduced number of paths of the master network graph. A source may be monitored to determine a cyber security threat by receiving incoming log file data related to the source, comparing the incoming log file data related to the source to the representative network graph, and determining, based on the comparison of the incoming log file data related to the source to the representative network graph, an anomaly in the representative network graph. Further, based on the monitoring, a report indicative of the cyber security threat may be generated based on the anomaly in the representative network graph.