Domain expert heuristics are captured within a computational framework for a task scheduling system. One or more classifiers are trained to predict (i) whether a first action should be scheduled instead of a second action using pairwise comparisons between actions scheduled by a demonstrator at particular times and actions not scheduled by the demonstrator at the particular times, and (ii) whether a particular action should be scheduled for a particular agent at a particular time. The system then generates a schedule for a set of actions to be performed by a plurality of agents using a plurality of resources over a plurality of time steps, by using the one or more classifiers to determine (i) a highest priority action in the set of actions, and (ii) whether the highest priority action should be scheduled for a particular agent at a particular time step.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for task scheduling using domain expert heuristics captured within a computational framework, the method performed on at least one computer having a memory and a processor executing instructions stored in the memory, the method comprising: training one or more classifiers to predict (i) whether a first action should be scheduled instead of a second action using pairwise comparisons between actions scheduled by a demonstrator at particular times and actions not scheduled by the demonstrator at the particular times, and (ii) whether a particular action should be scheduled for a particular agent at a particular time; and generating a schedule for a set of actions to be performed by a plurality of agents using a plurality of resources over a plurality of time steps, wherein generating the schedule comprises using the one or more classifiers to determine (i) a highest priority action in the set of actions, and (ii) whether the highest priority action should be scheduled for a particular agent at a particular time step. 2. The method of claim 1, wherein training the one or more classifiers further comprises: receiving a set of observations occurring over a plurality of times for a training action set, each observation comprising (i) features describing a state of each action in the training action set at one of the times and (ii) information identifying an action in the training action set scheduled at that time by a demonstrator, if any; and training the one or more classifiers based at least in part on the observations. 3. The method of claim 2, wherein training the one or more classifiers further comprises transforming each observation into a set of new observations by performing pairwise comparisons between the action scheduled by the demonstrator at that time and other actions not scheduled by the demonstrator at that time. 4. The method of claim 3, wherein performing the pairwise comparisons comprises creating a positive example for each observation in which an action was scheduled by computing a difference between corresponding values in a first feature vector describing a scheduled action in that observation and a second feature vector describing an unscheduled action in that observation. 5. The method of claim 3, wherein performing the pairwise comparisons comprises creating a negative example for each observation in which an action was not scheduled by computing a difference between corresponding values in a first feature vector describing an unscheduled action in that observation and a second feature vector describing a scheduled action in that observation. 6. The method of claim 1, wherein the one or more classifiers are trained using positive examples from observations in a set of observations in which an action was scheduled and negative examples from observations in a set of observations in which no action was scheduled. 7. The method of claim 1, wherein the resources comprise resources shared among the agents. 8. The method of claim 1, wherein each action in the set of actions comprises a task, an agent, and a resource. 9. The method of claim 1, wherein each action in the set of actions comprises one or more scheduling-relevant features including deadline, earliest time available, precedence, duration, resource required, and dependence on other action. 10. The method of claim 1, further comprising configuring the plurality of agents to perform the set of actions according to the schedule. 11. A system for task scheduling using domain expert heuristics captured within a computational framework, the system comprising: at least one memory for storing computer-executable instructions; and at least one processor for executing the instructions stored on the at least one memory, wherein execution of the instructions programs the at least one processor to perform operations comprising: training one or more classifiers to predict (i) whether a first action should be scheduled instead of a second action using pairwise comparisons between actions scheduled by a demonstrator at particular times and actions not scheduled by the demonstrator at the particular times, and (ii) whether a particular action should be scheduled for a particular agent at a particular time; and generating a schedule for a set of actions to be performed by a plurality of agents using a plurality of resources over a plurality of time steps, wherein generating the schedule comprises using the one or more classifiers to determine (i) a highest priority action in the set of actions, and (ii) whether the highest priority action should be scheduled for a particular agent at a particular time step. 12. The system of claim 11, wherein training the one or more classifiers further comprises: receiving a set of observations occurring over a plurality of times for a training action set, each observation comprising (i) features describing a state of each action in the training action set at one of the times and (ii) information identifying an action in the training action set scheduled at that time by a demonstrator, if any; and training the one or more classifiers based at least in part on the observations. 13. The system of claim 12, wherein training the one or more classifiers further comprises transforming each observation into a set of new observations by performing pairwise comparisons between the action scheduled by the demonstrator at that time and other actions not scheduled by the demonstrator at that time. 14. The system of claim 13, wherein performing the pairwise comparisons comprises creating a positive example for each observation in which an action was scheduled by computing a difference between corresponding values in a first feature vector describing a scheduled action in that observation and a second feature vector describing an unscheduled action in that observation. 15. The system of claim 13, wherein performing the pairwise comparisons comprises creating a negative example for each observation in which an action was not scheduled by computing a difference between corresponding values in a first feature vector describing an unscheduled action in that observation and a second feature vector describing a scheduled action in that observation. 16. The system of claim 11, wherein the one or more classifiers are trained using positive examples from observations in a set of observations in which an action was scheduled and negative examples from observations in a set of observations in which no action was scheduled. 17. The system of claim 11, wherein the resources comprise resources shared among the agents. 18. The system of claim 11, wherein each action in the set of actions comprises a task, an agent, and a resource. 19. The system of claim 11, wherein each action in the set of actions comprises one or more scheduling-relevant features including deadline, earliest time available, precedence, duration, resource required, and dependence on other action. 20. The system of claim 11, wherein the operations further comprise configuring the plurality of agents to perform the set of actions according to the schedule.