Utilizing machine learning to predict success of troubleshooting actions for repairing assets

1. An apparatus comprising:

at least one processing device comprising a processor coupled to a memory;

the at least one processing device being configured to perform steps of:

obtaining information regarding a given asset to be repaired;

generating a recommended troubleshooting action to be performed on the given asset;

providing the recommended troubleshooting action and the obtained information regarding the given asset as input to an encoder of a machine learning model, the machine learning model implementing an attention mechanism;

receiving, from a decoder of the machine learning model, a predicted success of the recommended troubleshooting action;

determining whether the predicted success of the recommended troubleshooting action meets one or more designated criteria;

performing the recommended troubleshooting action responsive to determining that the predicted success of the recommended troubleshooting action meets the one or more designated criteria; and

responsive to determining that the predicted success of the recommended troubleshooting action does not meet the one or more designated criteria, modifying the recommended troubleshooting action and repeating the providing, receiving and determining steps.

2. The apparatus of claim 1 wherein the information regarding the given asset to be repaired comprises one or more symptom sets, a given one of the one or more symptom sets comprising an identifier of the given asset, a description of the given asset, and a description of at least one error encountered on the given asset.

3. The apparatus of claim 2 wherein the information regarding the given asset to be repaired further comprises result information regarding the success or failure of one or more troubleshooting actions previously performed on the given asset.

4. The apparatus of claim 1 wherein the machine learning model comprises a sequence-to-sequence machine learning model.

5. The apparatus of claim 1 wherein the attention mechanism of the machine learning model is configured to focus the decoder on one or more portions of the input to the encoder.

6. The apparatus of claim 1 wherein generating the recommended troubleshooting action to be performed on the given asset comprises:

providing the obtained information regarding the given asset as input to an additional encoder of an additional machine learning model; and

receiving, from an additional decoder of the additional machine learning model, the recommended troubleshooting action.

7. The apparatus of claim 6 wherein modifying the recommended troubleshooting action comprises:

providing the obtained information regarding the given asset and feedback regarding the recommended troubleshooting action as input to the additional encoder of the additional machine learning model; and

receiving, from the additional decoder of the additional machine learning model, the modified recommended troubleshooting action.

8. The apparatus of claim 1 wherein determining whether the predicted success of the recommended troubleshooting action meets the one or more designated criteria comprises determining whether the predicted success of the recommended troubleshooting action meets a confidence score for each of one or more key performance indicators.

9. The apparatus of claim 8 wherein the recommended troubleshooting action comprises a diagnostic action, and wherein the one or more key performance indicators comprise at least one of:

an effectiveness in diagnosing one or more errors encountered by the given asset;

a complexity of performing the diagnostic action; and

a cost of performing the diagnostic action.

10. The apparatus of claim 8 wherein the recommended troubleshooting action comprises a repair action, and wherein the one or more key performance indicators comprise at least one of:

a likelihood of the repair action resulting in a verified fault of one or more components of the given asset;

a complexity of performing the repair action; and

a cost of performing the repair action.

11. The apparatus of claim 8 wherein determining whether the predicted success of the recommended troubleshooting action meets the one or more designated criteria further comprises, responsive to determining that the predicted success of the recommended troubleshooting action does not meet the confidence score for each of one or more key performance indicators, determining whether the recommended troubleshooting action is associated with at least one policy from a policy database.

12. The apparatus of claim 11 wherein responsive to determining that the recommended troubleshooting action is not associated with at least one policy from the policy database, providing a notification to a technician responsible for repairing the given asset, the notification indicating the predicted success of the recommended troubleshooting action.

13. The apparatus of claim 11 wherein responsive to determining that the recommended troubleshooting action is associated with at least one policy from the policy database, applying the at least one policy to determine whether to perform the recommended troubleshooting action or modify the recommended troubleshooting action.

14. The apparatus of claim 1 wherein the given asset comprises a computing device, wherein the recommended troubleshooting action comprises a repair action, and wherein the repair action comprises modifying at least one of: one or more software components of the computing device; and one or more hardware components of the computing device.

15. A method comprising steps of:

obtaining information regarding a given asset to be repaired;

generating a recommended troubleshooting action to be performed on the given asset;

providing the recommended troubleshooting action and the obtained information regarding the given asset as input to an encoder of a machine learning model, the machine learning model implementing an attention mechanism;

receiving, from a decoder of the machine learning model, a predicted success of the recommended troubleshooting action;

determining whether the predicted success of the recommended troubleshooting action meets one or more designated criteria;

performing the recommended troubleshooting action responsive to determining that the predicted success of the recommended troubleshooting action meets the one or more designated criteria; and

responsive to determining that the predicted success of the recommended troubleshooting action does not meet the one or more designated criteria, modifying the recommended troubleshooting action and repeating the providing, receiving and determining steps;

wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

16. The method of claim 15 wherein determining whether the predicted success of the recommended troubleshooting action meets the one or more designated criteria comprises determining whether the predicted success of the recommended troubleshooting action meets a confidence score for each of one or more key performance indicators.

17. The method of claim 15 wherein determining whether the predicted success of the recommended troubleshooting action meets the one or more designated criteria further comprises, responsive to determining that the predicted success of the recommended troubleshooting action does not meet the confidence score for each of one or more key performance indicators, determining whether the recommended troubleshooting action is associated with at least one policy from a policy database.

18. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform steps of:

obtaining information regarding a given asset to be repaired;

generating a recommended troubleshooting action to be performed on the given asset;

providing the recommended troubleshooting action and the obtained information regarding the given asset as input to an encoder of a machine learning model, the machine learning model implementing an attention mechanism;

receiving, from a decoder of the machine learning model, a predicted success of the recommended troubleshooting action;

determining whether the predicted success of the recommended troubleshooting action meets one or more designated criteria;

performing the recommended troubleshooting action responsive to determining that the predicted success of the recommended troubleshooting action meets the one or more designated criteria; and

responsive to determining that the predicted success of the recommended troubleshooting action does not meet the one or more designated criteria, modifying the recommended troubleshooting action and repeating the providing, receiving and determining steps.

19. The computer program product of claim 18 wherein determining whether the predicted success of the recommended troubleshooting action meets the one or more designated criteria comprises determining whether the predicted success of the recommended troubleshooting action meets a confidence score for each of one or more key performance indicators.

20. The computer program product of claim 18 determining whether the predicted success of the recommended troubleshooting action meets the one or more designated criteria further comprises, responsive to determining that the predicted success of the recommended troubleshooting action does not meet the confidence score for each of one or more key performance indicators, determining whether the recommended troubleshooting action is associated with at least one policy from a policy database.