Abstract: An application automatically composed using natural language processing. A natural language input comprising one or more application requirements is received via an interface. The natural language input is parsed to extract one or more chunks, each chunk representing one of the application requirements, and at least one of the chunks representing at least one of one or more main functionalities described by the application requirements. A coarse architecture logically arranging the main functionalities to satisfy the application requirements is inferred according to the chunks. Existing assets corresponding to the chunks are identified, each asset associated with at least one of the main functionalities. The identified assets are assembled according to the coarse architecture. The assembled assets are deployed as an application.

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: Techniques regarding generating and/or training one or more symbolic models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a training component that can train a symbolic model via active machine learning. The symbolic model can characterize a formal planning language for a planning domain as a plurality of digital image sequences.

Abstract: In an approach for generating and recommending optimized shopping orders for a group of users that collectively purchase bundles of goods, a processor generates an initial shopping order for each user in a group of shopping users, based on one or more preferences and constraints of each user on one or more items to buy from a stock. A processor optimizes the initial shopping order for each user based on one or more objectives of each user. A processor outputs the optimized shopping order for each user.

Abstract: In an approach for recommending a safe path for people to navigate an environment, a processor generates a contact graph for a user in a group of users in an area, based on a pre-defined distance measurement between the user and another user in a same timestamp during a pre-defined time period. A processor builds a profile for the user based on the contact graph, the profile including a probability model corresponding to the contact graph to estimate an infection probability for the user. A processor calculates an initial route for the user from a first location to a second location in the area. A processor analyzes an infection risk for the user based on the profile and other users within a pre-defined distance to the user in the initial route. A processor updates the initial route based on the analysis to minimize the risk to be infected.

Abstract: A method, system, and computer program product for learning parameters of Bayesian network using uncertain evidence, the method comprising: receiving input comprising graph representation and at least one sample of a Bayesian network, the graph comprising plurality of nodes representing random variables and plurality of directed edges representing conditional dependencies, wherein each of the at least one sample comprising for each node a value selected from the group consisting of: a known value; an unknown value; and an uncertain value; and applying on the input a Bayesian network learning process configured for calculating estimates of conditional probability tables of the Bayesian network using probabilities inferred by applying on the input a Bayesian network uncertain inference process configured for performing inference in a Bayesian network from uncertain evidence.

Abstract: In an approach to predictive contact tracing, a computer receives a query associated with contact tracing of a person with an infection. A computer retrieves timestamped location data associated with the person over a period of time. Based on the retrieved data, a computer creates a contact graph associated with the person, where the contact graph depicts one or more other people that were in contact with the person over the period of time. A computer retrieves medical data associated with the person and the one or more other people that were in contact with the person over the period of time. Based on the retrieved data, a computer builds a probabilistic model. A computer runs the probabilistic model to provide a prediction of a probability of infection of the one or more other people over the period of time as a result of being in contact with the person.

Abstract: A computer generates a formal planning domain description. The computer receives a first text-based description of a domain in an AI environment. The domain includes an action and an associated attribute, and the description is written in natural language. The computer receives the first text-based description of said domain and extracts a first set of domain actions and associated action attributes. The computer receives audio-visual elements depicting the domain, generates a second text-based description, and extracts a second set of domain actions and associated action attributes. The computer constructs finite state machines corresponding to the extracted actions and attributes. The computer converts the FSMs into a symbolic model, written in a formal planning language, that describes the domain.

Abstract: A historical log of a plurality of a type of Internet-connected appliances is collected. a dynamic probabilistic graphical model of the time-lined interactions between subsystems and components of the appliances is generated. The probabilistic graphical model is trained by applying machine learning techniques and applying expert knowledge. A natural language processing (NLP) conversational user interface for diagnostic queries regarding operational errors is provided, and responsive to receive a diagnostic query in a conversational format from a user regarding a particular type, make, and model of an appliance of the plurality of the type of Internet-connected appliances, a diagnostic response is provided to the user interface using the natural language processing conversational format.

Abstract: A system and method for interactive training for application providers in a computing environment are presented. A proposed application solution from a user for a selected application may be compared to one or more optimized solutions to identify one or more differences in the proposed application solution. One or more missing assets may be identified from the proposed application solution according to the one or more differences. The user may be surveyed with a survey relating to the missing assets such that survey results are used to train and develop a level of expertise for the user.

Abstract: Embodiments for relationship graph expansion and extraction from a collection of unstructured text data by a processor. A query relating to one or more concepts may be received. The query may be expanded according to a logical reasoning operation and a domain ontology having a set of logical rules. A relationship graph between one or more concepts from a plurality of unstructured text data may be extracted based on an expanded query according to a domain ontology and the set of logical rules.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate automated generation of a machine learning pipeline based on a pipeline grammar are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a pipeline structure generator component that generates a machine learning pipeline structure based on a pipeline grammar. The computer executable components can further comprise a pipeline optimizer component that selects one or more machine learning modules that achieve a defined objective to instantiate a machine learning pipeline based on the machine learning pipeline structure.

Abstract: Embodiments for intelligent career planning actions in a computing environment by a processor. A career planning model may be created for a user according to a career goal, a user profile, and one or more alternative user profiles and historical data of alternative users having achieved the career goal. A career plan may be generated for the user according to the career planning model.

Abstract: Embodiments for intelligent application scenario testing and error detection by a processor. One or more modified application scenarios may be automatically generated from an initial application scenario having configuration data and a plurality of operations relating to an error. The one or more modified application scenarios are variations of the initial application. The one or more modified application scenarios may be executed to detect the existence or non-existence of the error in the one or more modified application scenarios.

Abstract: Embodiments for recommending meals by a processor. A collaboration of data capturing a plurality of factors of a group user profile for each user in a group of users may be received for aiding in recommending one or more meals. The one or more meals may be recommended for the group of users according to the group user profile such that the recommending balances a satisfaction level for the one or more meals for the group of users.

Abstract: Systems, computer-implemented methods and/or computer program products that facilitate answering questions that involve mathematical computations are provided. In one embodiment, a computer-implemented method comprises: transforming, by a system operatively coupled to a processor, a natural language query into a first logical representation and extrinsic knowledge into a second logical representation relevant to the natural language query; merging, by the system, the first logical representation and the second logical representation into a third logical representation; and generating, by the system, answers for the natural language query based on processing of the third logical representation.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems to receive information pertaining to one or more tasks. Embodiments of the present invention can be used to predict a communication time at which a user is available is based, at least in part, on position movements of the user, sentiment of the user, and urgency of a task in the one or more tasks. Embodiments of the present invention can be used to, in response to confirming user availability, select a task from the one or more tasks and initiating a communication event for the task at the predicted communication time.

Abstract: Systems, computer-implemented methods and/or computer program products that facilitate answering questions that involve mathematical computations are provided. In one embodiment, a computer-implemented method comprises: transforming, by a system operatively coupled to a processor, a natural language query into a first logical representation and extrinsic knowledge into a second logical representation relevant to the natural language query; merging, by the system, the first logical representation and the second logical representation into a third logical representation; and generating, by the system, answers for the natural language query based on processing of the third logical representation.

Abstract: Various embodiments are provided for applying judgment reasoning knowledge in a dialog system in a computing environment by a processor. A determination is made that a response to a query during a dialog using the dialog system fails to comply with one or more expected response patterns to one of a plurality of query responses. An updated response may be provided to the query using judgment reasoning knowledge for matching the updated response with the one or more expected response patterns.