Knowledge based artificial intelligence architecture for industrial systems

Abstract

A system makes predictions using a machine learning model combined with a knowledge model. The system provides input data to a knowledge model and a machine learning based model. The machine learning based model is trained to make predictions based on input data. The system provides the outputs of the machine learning based model and the knowledge model to an ensemble model configured to combine results of the knowledge model and the machine learning based model. The system can be used for several applications. For example, the system may classify an input text based on a hierarchy of categories. The system may perform fault detection in time series data by identifying an anomaly data point and predicting whether the anomaly data point is a fault.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 receiving a request for making a prediction based on an input data;   providing the input data to a knowledge model, wherein the knowledge model is a rule-based model, wherein each rule makes s prediction based on one or more characteristics of the input data;   providing the input data to a machine learning based model, the machine learning based model trained to make the prediction based on a particular input data;   executing the knowledge model to generate a first output representing a first prediction for the input data;   executing the machine learning based model to generate a second output representing a second prediction for the input data;   providing the first output and the second output to an ensemble model configured to combine results of the knowledge model and the machine learning based model;   executing the ensemble model to determine a final output based on a combination of the first output and the second output; and   providing the final output as the prediction based on the input data.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the input data represents operational data from an industrial process, and a rule represents domain knowledge associated with the industrial process. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein determining the final output by the ensemble model comprises:
 receiving a measure of accuracy of the second output generated by the machine learning based model; and   responsive to the measure of accuracy of the second output of the machine learning based model indicating an accuracy below a threshold value, using the second output as the final output.   
     
     
         4 . The computer-implemented method of  claim 1 , wherein determining the final output by the ensemble model comprises:
 receiving a first measure of accuracy of the first output generated by the knowledge model;   receiving a second measure of accuracy of the second output generated by the machine learning based model; and   determining the final output based on the combination of the first output and the second output based on at least one of the first measure of accuracy or the second measure of accuracy.   
     
     
         5 . The computer-implemented method of  claim 4 , wherein the final output is based on the first output if a comparison of the first measure of accuracy and the second measure of accuracy indicates that the first output of the knowledge model has higher accuracy compared to the second output of the machine learning based model. 
     
     
         6 . The computer-implemented method of  claim 4 , wherein the final output is based on the second output if a comparison of the first measure of accuracy and the second measure of accuracy indicates that the first output of the knowledge model has lower accuracy compared to the second output of the machine learning based model. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the final output is a weighted aggregate of the first output and the second output, wherein a weight of each of the first output and the second output is determined based on a measure of accuracy of corresponding output. 
     
     
         8 . The computer-implemented method of  claim 1 , wherein determining the final output by the ensemble model comprises:
 responsive to determining the final output based on the first output of the knowledge model, using the final output for training of the machine learning based model.   
     
     
         9 . The computer-implemented method of  claim 8 , further comprising:
 generating synthetic data as additional training data for the machine learning based model using the final output.   
     
     
         10 . The computer-implemented method of  claim 1 , wherein the input data is sensor data collected by a robot, wherein the final output is used for guiding an action performed by the robot. 
     
     
         11 . The computer-implemented method of  claim 1 , wherein the input data represents data collected by an industrial process, wherein the final output is used for determining an action performed by the industrial process. 
     
     
         12 . A non-transitory computer readable storage medium storing instructions that when executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:
 receiving a request for making a prediction based on an input data;   providing the input data to a knowledge model, wherein the knowledge model is a rule-based model, wherein each rule makes s prediction based on one or more characteristics of the input data;   providing the input data to a machine learning based model, the machine learning based model trained to make the prediction based on a particular input data;   executing the knowledge model to generate a first output representing a first prediction for the input data;   executing the machine learning based model to generate a second output representing a second prediction for the input data;   providing the first output and the second output to an ensemble model configured to combine results of the knowledge model and the machine learning based model;   executing the ensemble model to determine a final output based on a combination of the first output and the second output; and   providing the final output as the prediction based on the input data.   
     
     
         13 . The non-transitory computer readable storage medium of  claim 12 , wherein instructions for determining the final output by the ensemble model cause the one or more computer processors to perform steps comprising:
 receiving a measure of accuracy of the second output generated by the machine learning based model; and   responsive to the measure of accuracy of the second output of the machine learning based model indicating an accuracy below a threshold value, using the second output as the final output.   
     
     
         14 . The non-transitory computer readable storage medium of  claim 12 , wherein instructions for determining the final output by the ensemble model cause the one or more computer processors to perform steps comprising:
 receiving a first measure of accuracy of the first output generated by the knowledge model;   receiving a second measure of accuracy of the second output generated by the machine learning based model; and   determining the final output based on the combination of the first output and the second output based on at least one of the first measure of accuracy or the second measure of accuracy.   
     
     
         15 . The non-transitory computer readable storage medium of  claim 14 , wherein the final output is based on the first output if a comparison of the first measure of accuracy and the second measure of accuracy indicates that the first output of the knowledge model has higher accuracy compared to the second output of the machine learning based model. 
     
     
         16 . The non-transitory computer readable storage medium of  claim 14 , wherein the final output is based on the second output if a comparison of the first measure of accuracy and the second measure of accuracy indicates that the first output of the knowledge model has lower accuracy compared to the second output of the machine learning based model. 
     
     
         17 . The non-transitory computer readable storage medium of  claim 12 , wherein the final output is a weighted aggregate of the first output and the second output, wherein a weight of each of the first output and the second output is determined based on a measure of accuracy of corresponding output. 
     
     
         18 . The non-transitory computer readable storage medium of  claim 12 , wherein instructions for determining the final output by the ensemble model cause the one or more computer processors to perform steps comprising:
 responsive to determining the final output based on the first output of the knowledge model, using the final output for training of the machine learning based model.   
     
     
         19 . The non-transitory computer readable storage medium of  claim 18 , wherein the instructions cause the one or more computer processors to perform steps comprising:
 generating synthetic data as additional training data for the machine learning based model using the final output.   
     
     
         20 . A computer system comprising:
 one or more computer processors; and   a non-transitory computer readable storage medium storing instructions that when executed by the one or more computer processors, cause the one or more computer processors to perform steps comprising:
 receiving a request for making a prediction based on an input data; 
 providing the input data to a knowledge model, wherein the knowledge model is a rule-based model, wherein each rule makes s prediction based on one or more characteristics of the input data; 
 providing the input data to a machine learning based model, the machine learning based model trained to make the prediction based on a particular input data; 
 executing the knowledge model to generate a first output representing a first prediction for the input data; 
 executing the machine learning based model to generate a second output representing a second prediction for the input data; 
 providing the first output and the second output to an ensemble model configured to combine results of the knowledge model and the machine learning based model; 
 executing the ensemble model to determine a final output based on a combination of the first output and the second output; and 
 providing the final output as the prediction based on the input data.