Methods and systems of using machine learning to create a trusted model that improves the operation of a computer system controller are provided herein. In some embodiments, a machine learning method includes training a model using input data, extracting the model, and determining whether the model satisfies the trust-related constraints. If the model does not satisfy the trust-related constraint, modifying at least one of: the model using one or more model repair algorithms, the input data using one or more data repair algorithms, or a reward function of the model using one or more reward repair algorithms, and re-training the model using at least one of the modified model, the modified input data, or the modified reward function. If the model satisfies the trust-related constraints, providing the model as a trusted model that enables a computer system controller to perform system actions within predetermined guarantees.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of using machine learning to create a trusted model that improves operation of a computer system controller, the method comprising: (a) determining at least one trust-related constraint related to at least one system action that could be performed by the computer system controller; (b) training a model of the computer system controller using an initial set of input data; (c) extracting the model; (d) determining whether the model satisfies the at least one trust-related constraint; (e) when it is determined that the model does not satisfy the at least one trust-related constraint: (1) modifying at least one of: the model using one or more model repair algorithms, the input data using one or more data repair algorithms, or a reward function of the model using one or more reward repair algorithms; and (2) re-training the model using at least one of the modified model, the modified input data, or the modified reward function; and (f) when it is determined that the model satisfies the at least one trust-related constraint, providing the model as a trusted model that improves the operation of the computer system controller by enabling the computer system controller to perform the at least one system action within predetermined guarantees. 2. The machine learning method of claim 1, wherein the at least one trust-related constraint is a temporal logic constraint. 3. The machine learning method of claim 1, wherein the model is initially trained using one of a maximum likelihood inverse reinforcement learning methodology, a supervised learning, or unsupervised learning. 4. The machine learning method of claim 1, wherein the model includes a plurality of states and transitions between states, and wherein the one or more model repair algorithms includes at least one of splitting states, increasing the size of a finite-state control, adding or removing transitions, or changing probabilities of transitions from one state to another state. 5. The machine learning method of claim 1, wherein the one or more model repair algorithms produces a modified model that minimizes a cost function while satisfying the at least one trust-related constraint. 6. The machine learning method of claim 1, wherein the model repair algorithm consists of at least a parametric model checker and a non-linear optimizer. 7. The machine learning method of claim 1, wherein determining whether the model satisfies the at least one trust-related constraint includes: determining a trust score of the model indicating of how well the model satisfies the at least one trust-related constraint, wherein the model is determined to satisfy the at least one trust-related constraint when the trust score is greater than a predefined trust threshold, and wherein the model is determined to not satisfy the at least one trust-related constraint when the trust score is less than a predefined trust threshold. 8. The machine learning method of claim 7, further comprising: determining the trust score of the re-trained model; if the trust score of the re-trained model is less than a predefined trust threshold, than at least one of: (A) sending a notification to the computer system controller that no further modification is possible and that the model is not trusted; or (B) repeating steps (c)-(e) until it is determined that the model satisfies the at least one trust-related constraint; and if the trust score of the re-trained model is greater than a predefined trust threshold, determining that the model satisfies the at least one trust-related constraint. 9. The machine learning method of claim 1, wherein the one or more data repair algorithms includes at least one of adding data, removing data, filtering data, adjusting data, adding data features, or removing data features. 10. The machine learning method of claim 1, wherein the one or more data repair algorithms minimizes a cost function while satisfying the at least one trust-related constraint. 11. The machine learning method of claim 10, wherein modifying the input data using the one or more data repair algorithms includes dropping data points that are determined to be noisy or include errors, and keeping reliable data points. 12. The machine learning method of claim 1, wherein the one or more reward repair algorithms includes modifying a reward function by projecting the reward function to a subspace where the at least one trust-related constraint is satisfied. 13. The machine learning method of claim 1, wherein the model is a probabilistic model, and wherein the probabilistic model is one of a discrete-time Markov Chain (DTMC) or Markov Decision Process (MDP). 14. The machine learning method of claim 1, wherein the trust-related constraints are received via a user interface. 15. The machine learning method of claim 14, wherein the user interface is configured to convert natural language specifications to logical formulas. 16. A machine learning system for creating a trusted model that improves operation of a computer system controller, comprising: a user interface configured to receive at least one trust-related constraint; a model training module configured to train a model using input data and machine learning algorithms; a model checking module configured to determine whether the model satisfies the at least one trust-related constraint; and a repair module, wherein the repair module includes at least one of: (a) a model repair module that modifies the model using one or more model repair algorithms; (b) a data repair module that modifies the input data using one or more data repair algorithms; or (c) a reward repair module that modifies a reward function of the model using one or more reward repair algorithms, wherein the machine learning system is configured to provide the trained model to the computer system controller as a trusted model that improves the operation of the computer system controller by enabling the computer system controller to perform the at least one system action within predetermined guarantees when it is determined that the model satisfies the at least one trust-related constraint. 17. The machine learning system of claim 16, wherein the model repair algorithm consists of at least a parametric model checker and a non-linear optimizer. 18. The machine learning system of claim 16, wherein the model is a probabilistic model, and wherein the probabilistic model is one of a discrete-time Markov Chain (DTMC) or Markov Decision Process (MDP). 19. The machine learning system of claim 16, wherein the user interface is configured to convert natural language specifications to logical formulas. 20. A non-transitory computer readable medium for storing computer instructions that, when executed by at least one processor causes the at least one processor to perform a method of using machine learning to create a trusted model that improves operation of a computer system controller, the method comprising: (a) determining at least one trust-related constraint related to at least one system action that could be performed by the computer system controller; (b) training a model of the computer system controller using an initial set of input data; (c) extracting the model; (d) determining whether the model satisfies the at least one trust-related constraint; (e) when it is determined that the model does not satisfy the at least one trust-related constraint: (1) modifying at least one of: the model using one or more model repair algorithms, the input data using one or more data repair algorithms, or a reward function of the model using one or more reward repair algorithms; and (2) re-training the model using at least one of the modified model, the modified input data, or the modified reward function; and (f) when it is determined that the model satisfies the at least one trust-related constraint, providing the model as a trusted model that improves the operation of the computer system controller by enabling the computer system controller to perform the at least one system action within predetermined guarantees. 21. A method for using machine learning to create a trusted model that improves operation of an autonomous vehicle controller, the method comprising: receiving a temporal driving constraint that must be satisfied before the autonomous vehicle controller can perform an action; training a probabilistic model of the autonomous vehicle controller using an initial set of input data; determining whether the probabilistic model satisfies the temporal driving constraint; when it is determined that the probabilistic model satisfies the temporal driving constraint, providing the model for use by the autonomous vehicle controller; when it is determined that the probabilistic model does not satisfy the temporal driving constraint: (1) modifying at least one of: the model using one or more model repair algorithms, the input data using one or more data repair algorithms, or a reward function of the model using one or more reward repair algorithms; and (2) re-training the model using at least one of the modified model, the modified input data, or the modified reward function; and (3) once it has been determined that the re-trained model satisfies the temporal driving constraint, providing the model to the autonomous vehicle controller as a trusted model that improves the operation of the autonomous vehicle controller by enabling the autonomous vehicle controller to perform the at least one system action within predetermined guarantees.