Various technologies described herein pertain to generating control inputs for a cyber-physical system. A prediction concerning a phenomenon can be generated, utilizing a classifier, based on sensor data acquired by a sensor. The prediction can include a probability distribution over a set of possible values of the phenomenon, where the phenomenon pertains to the cyber-physical system or an environment in which the cyber-physical system operates. Control inputs for the cyber-physical system that satisfy constraints that maintain safe operation of the cyber-physical system in the environment can be synthesized. The constraints can be based on the prediction that includes the probability distribution over the set of possible values of the phenomenon. Further, the cyber-physical system can be caused to operate in the environment based on the control inputs.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of controlling a cyber-physical system, comprising: generating, based on sensor data acquired by a sensor and utilizing a classifier, a prediction concerning a phenomenon, the prediction comprises a probability distribution over a set of possible values of the phenomenon, and the phenomenon pertains to the cyber-physical system or an environment in which the cyber-physical system operates; synthesizing control inputs for the cyber-physical system that satisfy constraints that maintain safe operation of the cyber-physical system in the environment, the constraints being based on the prediction that comprises the probability distribution over the set of possible values of the phenomenon; and causing the cyber-physical system to operate in the environment based on the control inputs. 2. The method of claim 1, causing the cyber-physical system to operate in the environment based on the control inputs further comprises: transmitting the control inputs to the cyber-physical system. 3. The method of claim 1, further comprising: updating the constraints based on changes over time to the probability distribution over the set of possible values of the phenomenon; and synthesizing the control inputs for the cyber-physical system that satisfy the constraints as updated over time. 4. The method of claim 1, synthesizing the control inputs for the cyber-physical system further comprises: synthesizing the control inputs that optimize a cost function and satisfy the constraints, wherein the cost function specifies an operation to be performed by the cyber-physical system. 5. The method of claim 1, wherein the cyber-physical system is one of a smart grid, an autonomous automotive system, a medical monitoring system, a process control system, a robotics system, or a quadcopter. 6. The method of claim 1, wherein the constraints are defined based on a probabilistic framework, and wherein the probabilistic framework provides semantics that enable incorporation of uncertainties in predicates and specification of temporal operators and Boolean operators to be applied to the predicates. 7. The method of claim 1, wherein the constraints are further based on at least one differing prediction generated utilizing at least one differing classifier, and wherein the prediction and the at least one differing prediction are combined utilizing one or more of a temporal operator or a Boolean operator. 8. The method of claim 1 being executed by the cyber-physical system. 9. The method of claim 1 being executed by a computing system separate from but in communication with the cyber-physical system. 10. The method of claim 1, wherein the classifier is one of a Bayesian classifier or a Bayesian linear regressor. 11. A computing system, comprising: at least one processor; and memory that comprises computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform acts including: providing a probabilistic framework, wherein the probabilistic framework enables constraints to be defined for synthesis of control inputs of a cyber-physical system; forming the constraints for the cyber-physical system based on the probabilistic framework, wherein the constraints are based on predictions that comprise probability distributions over sets of possible values of phenomena, wherein the phenomena pertain to at least one of the cyber-physical system or an environment in which the cyber-physical system operates; and synthesizing the control inputs for the cyber-physical system that satisfy the constraints. 12. The computing system of claim 11, wherein the probabilistic framework provides semantics that enable incorporation of uncertainties in predicates and specification of temporal operators and Boolean operators to be applied to the predicates. 13. The computing system of claim 12, wherein the memory further comprises computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform acts including: combining the predictions that comprise the probability distributions over the sets of possible values of the phenomena utilizing the temporal operators and the Boolean operators. 14. The computing system of claim 11, wherein the memory further comprises computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform acts including: generating, based on sensor data acquired by sensors and utilizing classifiers, the predictions that comprise the probability distributions over the sets of possible values of the phenomena. 15. The computing system of claim 11, wherein the memory further comprises computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform acts including: causing the cyber-physical system to operate in the environment based on the control inputs. 16. A method of controlling a cyber-physical system, comprising: combining a first prediction generated utilizing a first classifier and a second prediction generated utilizing a second classifier, wherein the first prediction comprises a first probability distribution and the second prediction comprises a second probability distribution, and wherein constraints that maintain safe operation of the cyber-physical system in an environment are based on a combination of the first prediction and the second prediction; synthesizing control inputs for the cyber-physical system that satisfy the constraints; and causing the cyber-physical system to operate in the environment based on the control inputs. 17. The method of claim 16, further comprising: generating, based on first sensor data acquired by a first sensor and utilizing the first classifier, the first probability distribution, wherein the first probability distribution concerns a first phenomenon, and wherein the first phenomenon pertains to the cyber-physical system or the environment in which the cyber-physical system operates; and generating, based on second sensor data acquired by a second sensor and utilizing the second classifier, the second probability distribution, wherein the second probability distribution concerns a second phenomenon, wherein the second phenomenon pertains to the cyber-physical system or the environment in which the cyber-physical system operates, and wherein the first phenomenon differs from the second phenomenon. 18. The method of claim 16, further comprising: generating, based on first sensor data acquired by a first sensor and utilizing the first classifier, the first probability distribution, wherein the first probability distribution concerns a particular phenomenon, and wherein the particular phenomenon pertains to the cyber-physical system or the environment in which the cyber-physical system operates; and generating, based on second sensor data acquired by a second sensor and utilizing the second classifier, the second probability distribution, and wherein the second probability distribution concerns the particular phenomenon. 19. The method of claim 16, further comprising: generating, based on sensor data acquired by a sensor and utilizing the first classifier, the first probability distribution; and generating, based on the sensor data acquired by the sensor and utilizing the second classifier, the second probability distribution. 20. The method of claim 16, further comprising: combining the first prediction and the second prediction utilizing one or more of a temporal operator or a Boolean operator provided by a probabilistic framework.