Abstract: The disclosed invention provides system and method for providing autonomous actions that are consistently applied to evolving missions in physical and virtual domains. The system includes a autonomy engine that is implanted in computing devices. The autonomy engine includes a sense component including one or more sensor drivers that are coupled to the one or more sensors, a model component including a world model, a decide component reacting to changes in the world model and generating a task based on the changes in the world model, and an act component receiving the task from the decide component and invoking actions based on the task. The sense component acquires data from the sensors and extracts knowledge from the acquired data. The model component receives the knowledge from the sense component, and creates or updates the world model based on the knowledge received from the sense component.

Abstract: The disclosed invention provides system and method for providing autonomous actions that are consistently applied to evolving missions in physical and virtual domains. The system includes a autonomy engine that is implanted in computing devices. The autonomy engine includes a sense component including one or more sensor drivers that are coupled to the one or more sensors, a model component including a world model, a decide component reacting to changes in the world model and generating a task based on the changes in the world model, and an act component receiving the task from the decide component and invoking actions based on the task. The sense component acquires data from the sensors and extracts knowledge from the acquired data. The model component receives the knowledge from the sense component, and creates or updates the world model based on the knowledge received from the sense component.

Abstract: Digital object library management systems and methods for machine learning applications are taught herein.

Abstract: Digital object library management systems and methods for machine learning applications are taught herein.

Abstract: A system and method for detecting anomalous activity, the method includes collecting data from a plurality of data sources, wherein each data source generates a data stream; harmonizing each data stream using a computer processor so that the harmonized data is in a common format; generating behavior models based on the harmonized data using the computer processor; analyzing the harmonized data at a first level using the behavior models and the computer processor to generate meta-events, wherein the meta-events represent anomalous behavior; analyzing the meta-events at a second level using the computer processor to determine if an alert should be issued; and when an alert should be issued, displaying the alert is disclosed.

Abstract: A system and method for detecting anomalous activity, the method includes collecting data from a plurality of data sources, wherein each data source generates a data stream; harmonizing each data stream using a computer processor so that the harmonized data is in a common format; generating behavior models based on the harmonized data using the computer processor; analyzing the harmonized data at a first level using the behavior models and the computer processor to generate meta-events, wherein the meta-events represent anomalous behavior; analyzing the meta-events at a second level using the computer processor to determine if an alert should be issued; and when an alert should be issued, displaying the alert is disclosed.

Abstract: Digital object library management systems and methods for machine learning applications are taught herein.