International Business Machines Corporation

Vera Liao

Biplav Srivastava

Yunfeng Zhang

Elizabeth Daly

Oznur Alkan

Matthew Scott Davis

Adi Ionel Botea

Akihiro Kishimoto

Radu Marinescu

Kartik Talamadupula

Rachel K E Bellamy

Stephanie Houde

A computer-implemented method for counterfactual conversation simulation is disclosed. The computer-implemented method includes generating a system output based, at least in part, on a user input. The computer-implemented method further includes determining that a system output/user input pair is not satisfactory based, at least in part, on a system output/user input score being below a predetermined threshold. The computer-implemented method further includes generating, in response to determining the system output/user input pair is not satisfactory, a counterfactual simulation of the user input based, at least in part, on a target intent of the user input.

Conversational agent counterfactual simulation

Techniques that facilitate generating and executing an optimal dialogue strategy are provided. In one example, a system includes an information gain component and a question selector component. The information gain component estimates information gain data associated with first dialogue data of an information system. The information gain data is indicative of an amount of change in entropy associated with the first dialogue data. The question selector component selects between second dialogue data associated with a first question strategy and third dialogue data associated with a second question strategy based on the information gain data.

Generating and executing an optimal dialogue strategy

Various embodiments are provided for integrating multiple domain learning and personalization in a dialog system for a user in a computing environment by a processor. One or more problem instances may be defined for multiple domains according to a problem instance template, identified user intent, links to one or more problem solvers associated with the multiple domains, or a combination thereof. A dialog plan may be determined to further define the one or more problem instances in response to user input. A solution may be provided to the user for the one or more problem instances.

Integrating multiple domain problem solving in a dialog system for a user

Various embodiments are provided for using a dialog system for integrating multiple domain learning and problem solving for a user in a computing environment by a processor. One or more problem instances may be defined 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. A dialog plan may be determined 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. A solution may be provided to the user for the one or more problem instances. One or more preferences of a user may be learned according to the solution.

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

Embodiments for building domain models from dialog interactions by a processor. A domain knowledge may be elicited from one or more dialog interactions with one or more users according to one or more dialog strategies. One or more domain models may be built and/or enhanced according to the domain knowledge.

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White Plains, NY, United States of America

Vera Liao