Automatic adjustment of constraints in task solution generation

A computer system for dynamic processing within a computing environment, the computer system comprising:

• a memory;
• and
• one or more processors in communication with the memory, wherein the computer system is configured to perform a method, said method comprising:
• executing an automated process on the one or more processors to dynamically generate a task solution for a selected event using data stored in a selected form in one or more data structures generated for the automated process by at least one processing engine of the one or more processors, the selected event being a complex real-world event and the selected form to facilitate retrieval and use of the data, the data including constraints data generated and stored in a data structure of the one or more data structures based on one or more defined tasks of the complex real-world event, the executing the automated process including:
• retrieving, by a dynamic feedback controller executing on at least one processor of the one or more processors, constraints data stored in the data structure, the constraints data including a set of constraints for a defined task to be performed for the selected event, the set of constraints having one or more levels of constraints generated and assigned thereto by a restriction condition sorter processing engine of the one or more processors, the one or more levels of constraints to be used in constraint prioritization, and the restriction condition sorter processing engine executing a selected machine learning model used in generating and assigning the one or more levels of constraints;
• sending, by the dynamic feedback controller to a solution generator executing on at least one processor of the one or more processors, the constraints data retrieved from the data structure generated by the at least one processing engine based on the defined task to be performed for the selected event, the solution generator to be used to obtain a task solution to specify how to perform the defined task for the selected event, the task solution to satisfy one or more defined criteria defined for the selected event;
• obtaining, by the dynamic feedback controller, one or more task solutions generated by the solution generator for the defined task, the one or more task solutions generated using the constraint data that is retrieved by the dynamic feedback controller from the data structure generated based on the defined task to be performed for the selected event;
• determining, by the dynamic feedback controller, whether the one or more task solutions include the task solution that satisfies the one or more defined criteria;
• automatically adjusting, by the dynamic feedback controller, the set of constraints to provide an adjusted set of constraints, based on determining that the one or more task solutions do not include the task solution that satisfies the one or more defined criteria, the dynamic feedback controller using in the automatically adjusting a machine learning model trained to automatically adjust the set of constraints, the machine learning model indicating one or more constraints of the set of constraints to be adjusted by the dynamic feedback controller;
• sending from the dynamic feedback controller to the solution generator the adjusted set of constraints that are adjusted using the machine learning model executed by the dynamic feedback controller, the adjusted set of constraints being provided by the dynamic feedback controller to the solution generator based on the automatically adjusting and received as input to the solution generator for use in generating one or more other task solutions, and wherein the adjusted set of constraints generated based on the automatically adjusting and using the machine learning model executed by the dynamic feedback controller is used to retrain the selected machine learning model executed by the restriction condition sorter processing engine used to generate and assign the one or more levels of constraint;
• iteratively performing, by the dynamic feedback controller one or more times, at least, the obtaining and the determining to obtain the task solution that satisfies the one or more defined criteria defined for the selected event, wherein the one or more task solutions of the obtaining and the determining include the one or more other task solutions;
• obtaining, by the dynamic feedback controller, the task solution to specify how to perform the defined task for the selected event;
• and
• outputting, by the dynamic feedback controller, at least an indication of the task solution, the task solution to be used to implement and perform the defined task for the selected event.

A computer program product for dynamic processing within a computing environment, said computer program product comprising:

• one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media readable by at least one processing circuit to perform a method comprising:
• executing an automated process on one or more processors of the computing environment to dynamically generate a task solution for a selected event using data stored in a selected form in one or more data structures generated for the automated process by at least one processing engine of the one or more processors, the selected event being a complex real-world event and the selected form to facilitate retrieval and use of the data, the data including constraints data generated and stored in a data structure of the one or more data structures based on one or more defined tasks of the complex real-world event, the executing the automated process including:
• retrieving, by a dynamic feedback controller executing on at least one processor of the one or more processors of the computing environment, the constraints data stored in the data structure, the constraints data including a set of constraints for a defined task to be performed for the selected event the set of constraints having one or more levels of constraints generated and assigned thereto by a restriction condition sorter processing engine of the one or more processors, the one or more levels of constraints to be used in constraint prioritization, and the restriction condition sorter processing engine executing a selected machine learning model used in generating and assigning the one or more levels of constraints;
• sending, by the dynamic feedback controller to a solution generator executing on at least one processor of the one or more processors, the constraints data retrieved from the data structure generated by the at least one processing engine based on the defined task to be performed for the selected event, the solution generator to be used to obtain a task solution to specify how to perform the defined task for the selected event, the task solution to satisfy one or more defined criteria defined for the selected event;
• obtaining, by the dynamic feedback controller, one or more task solutions generated by the solution generator for the defined task, the one or more task solutions generated using the constraints data that is retrieved by the dynamic feedback controller from the data structure generated based on the defined task to be performed for the selected event;
• determining, by the dynamic feedback controller, whether the one or more task solutions include the task solution that satisfies the one or more defined criteria;
• automatically adjusting, by the dynamic feedback controller, the set of constraints to provide an adjusted set of constraints, based on determining that the one or more task solutions do not include the task solution that satisfies the one or more defined criteria, the dynamic feedback controller using in the automatically adjusting a machine learning model trained to automatically adjust the set of constraints, the machine learning model indicating one or more constraints of the set of constraints to be adjusted by the dynamic feedback controller;
• sending from the dynamic feedback controller to the solution generator the adjusted set of constraints that are adjusted using the machine learning model executed by the dynamic feedback controller, the adjusted set of constraints being provided by the dynamic feedback controller to the solution generator based on the automatically adjusting and received as input to the solution generator for use in generating one or more other task solutions, and wherein the adjusted set of constraints generated based on the automatically adjusting and using the machine learning model executed by the dynamic feedback controller is used to retrain the selected machine learning model executed by the restriction condition sorter processing engine used to generate and assign the one or more levels of constraints;
• iteratively performing, by the dynamic feedback controller one or more times, at least, the obtaining and the determining to obtain the task solution that satisfies the one or more defined criteria defined for the selected event, wherein the one or more task solutions of the obtaining and the determining include the one or more other task solutions;
• obtaining, by the dynamic feedback controller, the task solution to specify how to perform the defined task for the selected event;
• and
• outputting, by the dynamic feedback controller, at least an indication of the task solution, the task solution to be used to perform the defined task for the selected event.