Knowledge augmented sequential decision-making under uncertainty

A computer-implemented method, comprising:

• training, by a system operatively coupled to a processor, using a data structure comprising tuples, an optimization model to generate a decision policy for a problem, wherein the tuples respectively comprise a state variable, an action variable, and a reward variable;
• analyzing, by the system, a desired constraint input provided in a natural language form;
• transforming, by the system, the desired constraint input provided in the natural language form into a constraint based deterministic probability function;
• generating, by the system, an influence mapping based on the optimization model, the data structure comprising the tuples, and the constraint based deterministic probability function;
• and
• retraining, by the system, using the influence mapping, the optimization model to generate an updated decision policy for the problem.

A computer program product facilitating a process to generate a decision policy, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• train, by the processor, using a data structure comprising tuples, an optimization model to generate the decision policy for a problem, wherein the tuples respectively comprise a state variable, an action variable, and a reward variable;
• transform, by the processor, the desired constraint input provided in the natural language form into a constraint based deterministic probability function;
• analyze, by the processor, a desired constraint input provided in a natural language form;
• generate, by the processor, an influence mapping based on the optimization model, the data structure comprising the tuples, and the constraint based deterministic probability function;
• and
• retrain, by the processor, using the influence mapping, the optimization model to generate an updated decision policy for the problem.