US 12,314,015 B2
Redundant machine learning architecture for high-risk environments
Kingsuk Maitra, Fremont, CA (US); Kinshumann Kinshumann, Redmond, WA (US); Garrett Patrick Prendiville, Sallins (IE); and Kence Anderson, Berkeley, CA (US)
Filed by MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed on Jun. 21, 2022, as Appl. No. 17/845,959.
Claims priority of provisional application 63/333,553, filed on Apr. 21, 2022.
Prior Publication US 2023/0341822 A1, Oct. 26, 2023
Int. Cl. G05B 13/02 (2006.01)
CPC G05B 13/0265 (2013.01) 20 Claims
OG exemplary drawing
 
1. A method comprising:
extracting, by one or more processing units, data defining a current state of an environment containing a plurality of components and a control system that is managed by a fault tolerant machine learning agent comprising a selector agent, a nominal agent, and a redundancy agent;
generating, by the nominal agent, a first action based on the data defining the current state of the environment, wherein the first action:
is generated irrespective of a failure condition of the plurality of components; and
presumes a normal function of the plurality of components;
generating, by the redundancy agent, a second action based on the data defining the current state of the environment, wherein the second action:
is generated irrespective of the failure condition of the plurality of components;
presumes a malfunction of the plurality of components; and
defines an operation for resolving the malfunction;
calculating, by the selector agent, a plurality of error metrics based on the data defining the current state of the environment;
determining, by the selector agent, whether one or more of the plurality of error metrics meets or exceeds a threshold error metric indicating the failure condition of the plurality of components; and
selecting, by the selector agent, the first action generated by the nominal agent for application to the control system or the second action generated by the redundancy agent for application to the control system based on whether the one or more of the plurality of error metrics meets or exceeds the threshold error metric.