US Patent Application 17845959. REDUNDANT MACHINE LEARNING ARCHITECTURE FOR HIGH-RISK ENVIRONMENTS simplified abstract

REDUNDANT MACHINE LEARNING ARCHITECTURE FOR HIGH-RISK ENVIRONMENTS

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Kingsuk Maitra of Fremont CA (US)

Kinshumann Kinshumann of Redmond WA (US)

Garrett Patrick Prendiville of Sallins (IE)

Kence Anderson of Berkeley CA (US)

REDUNDANT MACHINE LEARNING ARCHITECTURE FOR HIGH-RISK ENVIRONMENTS - A simplified explanation of the abstract

• This abstract for appeared for US patent application number 17845959 Titled 'REDUNDANT MACHINE LEARNING ARCHITECTURE FOR HIGH-RISK ENVIRONMENTS'

Simplified Explanation

This abstract describes a technique that improves the reliability of autonomous control systems using a fault-tolerant machine learning architecture. The architecture consists of three components: a selector agent, a nominal agent, and a redundancy agent. The machine learning agent collects data from the control system and its components. The nominal and redundancy agents use this data to generate actions, which are then evaluated by the selector agent. If a failure is detected, the selector agent uses the redundancy agent's lookup table to resolve the issue and restore normal operations.

Original Abstract Submitted

The techniques disclosed herein enable systems to enhance the resilience of autonomous control systems through a fault-tolerant machine learning architecture. To achieve this, a fault-tolerant machine learning agent is constructed with a selector agent, a nominal agent, and a redundancy agent which is a multidimensional lookup table. The fault-tolerant machine learning agent extracts state data from an environment containing a control system and various components. The nominal agent and the redundancy agent generate actions for application to the control system based on the state data which are provided to the selector agent. Based on an analysis of the state data, the selector agent can detect a failure condition. In the event of a failure condition, the selector agent deploys the action generated by the redundancy agent lookup table to resolve the failure condition and restore normal operations.