Abstract: Automating generalization or personalization of conversational automation agents includes receiving, by computer hardware, a plurality of input conversations. The input conversations include, or are formed of, a plurality of utterances. A plurality of intents and slots are determined from the input conversations by processing the plurality of input conversations through a first classifier. A plurality of generalized intents are generated by performing entity recognition on the plurality of intents and slots using an entity recognizer. The entity recognizer is configured to apply a knowledge graph to the plurality of intents and slots. Slots of the plurality of input conversations as classified are masked to generate masked utterances. Conversational data, which includes the masked utterances and the plurality of generalized intents, are encoded as a plurality of feature vectors.

Abstract: A method, computer program, and computer system is provided for testing a user interface. A previously trained machine learning model trained with traces of interactions between one or more users and a user interface is accessed. The interactions include one or more timestamps of user interactions with the user interface, actions by each user associated with the user interface, and metadata associated with user interactions. A simulated interaction of a simulated agent utilizing the user interface is generated using the previously trained machine learning model. The simulated interaction is encoded as an input trace to a user interface. The encoded simulated interaction is input into the user interface for automated testing of the user interface. Results of the automated testing of the user interface are received.

Abstract: Techniques are provided for reinforcement learning software agents enhanced by external data. A reinforcement learning model supporting the software agent may be trained based on information obtained from one or more knowledge stores, such as online forums. The trained reinforcement learning model may be tested in an environment with limited connectivity to an external environment to meet performance criteria. The reinforcement learning software agent may be deployed with the tested and trained reinforcement learning model within an environment to autonomously perform actions to process requests.

Abstract: A computer system adapts processing of expressions by a command-line interface. An expression provided to the command-line interface is analyzed, wherein the command line interface includes pre-defined expression processing. One or more artificial intelligence agents are selected from a plurality of artificial intelligence agents based on the analysis of the expression. The expression is evaluated by the selected one or more artificial intelligence agents to determine processing modifications for the pre-defined expression processing. The expression is processed in accordance with the determined processing modifications and results are provided to the command-line interface. Embodiments of the present invention further include a method and program product for adapting processing of expressions by a shell in substantially the same manner described above.

Abstract: Machine learning methods and systems include training a teacher model on an environment. Action scores are generated for actions that can be performed within the environment using the teacher model. A student model is trained using pruned states of the environment. A policy is distilled by retraining the student model using labels from the teacher model and the teacher action scores.

Abstract: A method for assigning weights to a knowledge graph includes extracting information from a knowledge graph. The information including entities extracted from nodes of the knowledge graph and relations extracted from edges of the knowledge graph. A shortest path generator receives the extracted entities and relations, and potential assigned weights from a heuristic data repository. Weights for the edges of the knowledge graph are determined. The weights are assigned to the edges of the knowledge graph.

Abstract: One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text representation.

Abstract: A method of using artificial intelligence to provide source code from an original programming language in a target programming language showing regions of low confidence. The method includes receiving, by a computing device, a code base in an original programming language. The computing device further provides the code base in the original programming language to a target programming language using an artificial intelligence tool. The computing device additionally displays the code base in the target programming language using a visualization tool in a visual interface. The computing device still further displays the regions of uncertainty to a human user in the visual interface. The regions of uncertainty provide low confidence regions of the code base in the target programming language for targeted user intervention. The regions of low confidence correlate with violations to provide displayed actionable insight regions.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments for learning personalized actionable domain models by a processor. A domain model may be generated according to a plurality of actions, extracted from one or more online data sources, of a plurality of cluster representatives. The plurality of actions achieve a goal. A hierarchical action model may be generated based on probabilities of the domain model and the plurality of actions. The hierarchical action model comprises a sequence of actions of the plurality of actions for achieving the goal. The hierarchical action model may be personalized by filtering to a selected set of actions according to weighted actions of the plurality of actions.

Abstract: A method for assigning weights to a knowledge graph includes extracting information from a knowledge graph. The information including entities extracted from nodes of the knowledge graph and relations extracted from edges of the knowledge graph. A shortest path generator receives the extracted entities and relations, and potential assigned weights from a heuristic data repository. Weights for the edges of the knowledge graph are determined. The weights are assigned to the edges of the knowledge graph.

Abstract: One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text representation.

Abstract: A computer system adapts processing of expressions by a command-line interface. An expression provided to the command-line interface is analyzed, wherein the command line interface includes pre-defined expression processing. One or more artificial intelligence agents are selected from a plurality of artificial intelligence agents based on the analysis of the expression. The expression is evaluated by the selected one or more artificial intelligence agents to determine processing modifications for the pre-defined expression processing. The expression is processed in accordance with the determined processing modifications and results are provided to the command-line interface. Embodiments of the present invention further include a method and program product for adapting processing of expressions by a shell in substantially the same manner described above.

Abstract: Techniques are provided for reinforcement learning software agents enhanced by external data. A reinforcement learning model supporting the software agent may be trained based on information obtained from one or more knowledge stores, such as online forums. The trained reinforcement learning model may be tested in an environment with limited connectivity to an external environment to meet performance criteria. The reinforcement learning software agent may be deployed with the tested and trained reinforcement learning model within an environment to autonomously perform actions to process requests.

Abstract: Techniques for autonomously generating a domain model and/or an action model based on unstructured data are provided. In one example, a computer implemented method can comprise extracting, by a system operatively coupled to a processor, a plurality of actions from a non-numerical language. The plurality of actions can achieve a goal. The computer-implemented method can also comprise generating, by the system, a domain model based on the plurality of actions. Further, the computer-implemented method can comprise generating, by the system, an action model based on the domain model. In various embodiments, the action model can comprise an action transition for accomplishing the goal.

Abstract: Techniques for autonomously generating a domain model and/or an action model based on unstructured data are provided. In one example, a computer implemented method can comprise extracting, by a system operatively coupled to a processor, a plurality of actions from a non-numerical language. The plurality of actions can achieve a goal. The computer-implemented method can also comprise generating, by the system, a domain model based on the plurality of actions. Further, the computer-implemented method can comprise generating, by the system, an action model based on the domain model. In various embodiments, the action model can comprise an action transition for accomplishing the goal.

Abstract: 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.

Abstract: 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.

Abstract: A personal digital assistant device includes: a memory storing an interactive personal digital assistant program and a processor configured to execute the interactive personal digital assistant program. The interactive personal digital assistant program performs an operation to determine whether the service provider is automated or is not automated. The interactive personal digital assistant program is configured to issue a command to the service provider on behalf of a user of the device, when it is determined that the service provider is automated. The interactive personal digital assistant program is configured to issue an alert on the device when it is determined that the service provider is not automated. The interactive personal digital assistant program may continue until the goal of the interaction is met or human help is sought.