Using a dialog system for integrating multiple domain learning and problem solving

A system, for using a dialog system for integrating multiple domain learning and problem solving in a computing environment, comprising:

• one or more processors with executable instructions that when executed cause the system to:
• execute machine learning logic to generate a decision model, wherein the decision model is trained, in a first training stage, to define one or more problem instances for one or more selected domains in a multi-domain database according to a problem instance template, identified user intent, links to one or more problem solvers associated with the one or more selected domains, or a combination thereof;
• determine a dialog plan for the one or more problem instances using a dialog system associated with the multi-domain database, wherein each record in the multi-domain database corresponds to a selected database for the one or more selected domains, and wherein determining the dialog plan includes identifying a modeling cost-to-cognitive load sensitivity ratio of candidate solutions identified for the one or more problem instances, the modeling cost-to-cognitive load sensitivity ratio taking into consideration utility details of respective solutions of the candidate solutions correspondent to an overall cost of the respective solutions based on weights identified as preferable to the user;
• provide a solution to the user for the one or more problem instances;
• and
• learn one or more preferences of a user according to the solution.

A computer program product for, by one or more processors, using a dialog system for integrating multiple domain learning and problem solving in a computing environment, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

• an executable portion that executes machine learning logic to generate a decision model, wherein the decision model is trained, in a first training stage, to define one or more problem instances for one or more selected domains in a multi-domain database according to a problem instance template, identified user intent, links to one or more problem solvers associated with the one or more selected domains, or a combination thereof;
• an executable portion that determines a dialog plan for the one or more problem instances using a dialog system associated with the multi-domain database, wherein each record in the multi-domain database corresponds to a selected database for the one or more selected domains, and wherein determining the dialog plan includes identifying a modeling cost-to-cognitive load sensitivity ratio of candidate solutions identified for the one or more problem instances, the modeling cost-to-cognitive load sensitivity ratio taking into consideration utility details of respective solutions of the candidate solutions correspondent to an overall cost of the respective solutions based on weights identified as preferable to the user;
• an executable portion that provides a solution to the user for the one or more problem instances;
• and
• an executable portion that learns one or more preferences of a user according to the solutions.