An assistance strategy may be generated with a generating apparatus including a processor, and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to create a reward estimation model for estimating a reward for assisting at least one subject by analyzing a history of input by the subject, create a decision making model including a plurality of forms of assistance and estimated rewards for each form of assistance based on the reward estimation model and the history of input by the subject, and generate an assistance strategy based on the decision making model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A generating apparatus comprising: a processor; and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to: create a reward estimation model for estimating an estimated reward for assisting at least one subject by analyzing a history of input corresponding to the subject; create a decision making model including a plurality of forms of assistance and estimated rewards for each form of assistance based on the reward estimation model and the history of input corresponding to the subject; and generate an assistance strategy based on the decision making model. 2. The generating apparatus of claim 1, wherein the creation of the reward estimation model includes training the reward estimation model by using first learning data including a history of first sets, wherein each first set includes a form of assistance, the history of input and a corresponding reward indicated by at least one of the subject and an observer of the subject. 3. The generating apparatus of claim 2, wherein: the instructions further cause the processor to create a state estimation model for estimating an inner state of the subject by analyzing the history of input corresponding to the subject; and the decision making model is based further on a plurality of states, the state estimation model, and a plurality of probability distributions for state transitions caused by forms of assistance. 4. The generating apparatus of claim 3, wherein the creation of the state estimation model includes training the state estimation model by using second learning data including a history of second sets, wherein each second set includes the history of input and a corresponding inner state indicated by at least one of the subject and an observer of the subject. 5. The generating apparatus of claim 3, wherein the plurality of states is based on a combination of a class of assistance and a class of inner states. 6. The generating apparatus of claim 3, wherein the instructions further cause the processor to select an assistance to be presented to a target subject based on the assistance strategy. 7. The generating apparatus of claim 6, wherein the instructions further cause the processor to: observe input corresponding to the target subject; and estimate an estimated inner state of the target subject based on the state estimation model and the history of input corresponding to the target subject, and wherein the selection of the assistance is further based on the estimated inner state. 8. The generating apparatus of claim 6, wherein the selection of the assistance is further based on: a strategy to optimize the balance between exploitation of forms of assistance among the plurality of forms of assistance that are expected to be successful; and an exploration of forms of assistance that have not been validated to be successful. 9. The generating apparatus of claim 8, wherein the balance is optimized based on a Bayesian posterior distribution for the decision making model. 10. The generating apparatus of claim 6, wherein the instructions further cause the processor to update the decision making model based on a result of the selection of assistance. 11. The generating apparatus of claim 1, wherein: the input is to a device, and the assistance presents a help message through an interface of the device. 12. The generating apparatus of claim 11, wherein the reward is based on input selected from the group including of a difficulty in operating the device and an improvement of operation skill. 13. The generating apparatus of claim 1, wherein the creation of the decision making model includes decreasing the reward if mismatched assistance is presented. 14. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform the steps of: creating a reward estimation model for estimating a reward for assisting at least one subject by analyzing a history of input corresponding to the subject; creating a decision making model including a plurality of forms of assistance and estimated rewards for each form of assistance based on the reward estimation model and the history of input corresponding to the subject; and generating an assistance strategy based on the decision making model. 15. The computer program product of claim 14, wherein the operations further comprise: training the reward estimation model by using a first learning data including a history of first sets, each first set including a form of assistance, the history of input and a corresponding reward indicated by at least one of the subject and an observer of the subject. 16. The computer program product of claim 15, wherein the operations further comprise creating a state estimation model for estimating an inner state of the subject by analyzing the history of input corresponding to the subject, wherein the decision making model is based further on: a plurality of states; the state estimation model; and a plurality of probability distributions for state transitions cause by forms of assistance. 17. A computer-implemented method comprising: creating a reward estimation model for estimating a reward for assisting at least one subject by analyzing a history of input corresponding to the subject; creating a decision making model including a plurality of forms of assistance and estimated rewards for each form of assistance based on the reward estimation model and the history of input corresponding to the subject; and generating an assistance strategy, using a processor, based on the decision making model. 18. The method of claim 17, further comprising training the reward estimation model by using a first learning data including a history of first sets, each first set including: a form of assistance; the history of input; and a corresponding reward indicated by at least one of the subject and an observer of the subject. 19. The method of claim 18, further comprising: creating a state estimation model for estimating an inner state of the subject by analyzing the history of input corresponding to the subject, wherein the decision making model is based further on a plurality of states, the state estimation model, and a plurality of probability distributions for state transitions cause by forms of assistance.