Abstract: A method comprises determining a threshold based on a shape or size of a graphical user interface (GUI); selecting one or more sequences of digital actions (actions) as one or more compound actions from a list of sequences of actions, the one sequence of actions being performed by multiple computer devices of a plurality of computer devices, including at least one input device and at least one output device, the one or more sequences of actions having a total number of sequences that is no more than the threshold and satisfying a set of criteria related to a number of actions in each sequence of actions; creating a deep link as a shortcut representing a specific compound action, the shortcut when invoked causing performing the specific compound action and producing a specific result; causing presenting the shortcut as an UI element in the GUI.

Abstract: A data management server computer (“server”) is disclosed. The server receives action data associated with one or more users identifying a plurality of actions performed through one or more computer devices in a multi-modal networked environment, identifies useful sequences of actions as compound actions, and generates deep links representing the compound actions as UI elements which when invoked cause execution of the represented compound actions.

Abstract: A data management server computer (“server”) is disclosed. The server receives action data associated with one or more users identifying a plurality of actions performed through one or more computer devices in a multi-modal networked environment, identifies useful sequences of actions as compound actions, and generates deep links representing the compound actions as UI elements which when invoked cause execution of the represented compound actions.

Abstract: An exemplary method includes obtaining a group of classification labels and corresponding confidence values for at least one object identified within an image using a computer-based object recognition technique; generating a conversation, to resolve ambiguity among the classification labels, the generating including iteratively performing the following when (i) each of the confidence values is below a threshold value or (ii) two or more of the confidence values are above the threshold value: using a wordweb to identify properties that distinguish between a first one and a second one of the labels, ranking the properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first and second label based on user input received in response to the question; and when only one of the confidence values exceeds the threshold value, classifying the object using the label corresponding to the one confidence value.

Abstract: A first neural network can be trained to approximate a state-action value function to estimate an expected cumulative return for an agent to perform an action in a given state, the agent being an autonomous reinforcement learning agent running on the processor. A second neural network can be trained to generate a simulated experience, the second network trained to predict a simulated state at a next time step after performing a given action, the second neural network being trained using real experience in a real environment. The first neural network is trained based on the simulated experience and a real experience from a real environment. A selected action selected by the second neural network given a current state of the real environment can be performed. The agent can explore an action space by uniformly sampling an action from all possible remaining action-state space combinations and performing the sampled action.

Abstract: A method, a structure, and a computer system for tangible mixed reality. Exemplary embodiments may include identifying one or more real objects within an environment and identifying one or more interactions between the one or more real objects, one or more reactive objects, and one or more animations therebetween. The exemplary embodiments may further include generating one or more learning activities for a user of a mixed reality headset that include at least one of the one or more interactions, and deploying the one or more learning activities to the mixed reality headset.

Abstract: Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: obtaining a group of classification labels and corresponding confidence values for an object in an image; using a wordweb to determine one or more properties that distinguish between at least a first one of the classification labels and at least a second one of the classification labels within the group; selecting a first property from the properties to generate a question based on information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a belief threshold value.

Abstract: A computer-implemented method for a socially connectable agent. A non-limiting example of the computer-implemented method includes receiving, by a processor, input events. The method includes evaluating and enacting rules, by the processor, based on the received input events, where the rule include stochastic elements and temporal operators. The method pushes, by the processor, action events that result from the evaluation and enactment of the rules, and initiates, by the processor, action events.

Abstract: A method, a structure, and a computer system for tangible mixed reality. Exemplary embodiments may include identifying one or more real objects within an environment and identifying one or more interactions between the one or more real objects, one or more reactive objects, and one or more animations therebetween. The exemplary embodiments may further include generating one or more learning activities for a user of a mixed reality headset that include at least one of the one or more interactions, and deploying the one or more learning activities to the mixed reality headset.

Abstract: Techniques for assessing the proficiency of artificial intelligence agents and users in a given knowledge domain are described. A plurality of proficiency agents can be initialized with a plurality of proficiency scores, by performing a plurality of assessments between pairs of proficiency agents selected from the plurality of proficiency agents. A first client device associated with a first user is matched with a first proficiency agent of the plurality of proficiency agents, based on a first proficiency score associated with the first user and a second proficiency score of the plurality of proficiency scores corresponding to the first proficiency agent. Assessments results of an assessment performed between the first client device and the first proficiency agent are received, and a rating system update function is used to update the first proficiency score and the second proficiency score, based on the assessment results.

Abstract: Providing targeted, evidence-based recommendations to improve digital content quality, in one aspect, may include partitioning digital content into a sequence of logical content modules. Quality scores for the logical content modules along a plurality of quality dimensions may be determined. User interactions with the digital content on a user interface are monitored. Raw events generated from user interaction in accessing the digital content are captured. Raw event patterns for the logical content modules are detected. The raw event patterns of user interaction are transformed into a set of dynamic states representing potential user experience and performance. The set of dynamic states are associated with a respective logical content module. The quality dimensions of the logical content modules and associated quality scores are correlated with the dynamic states.

Abstract: A combination of knowledge representation and retrieval, assessments and personalization for a tutoring platform that provides an automated phased word exposition method for vocabulary learning and enable learners to learn words (or any learning objective) with sufficient proficiency and without excessive repetition. The phased learning objective method implements a moving average model that models the learner across multiple dimensions (e.g. reading, listening, writing, speaking) such that the platform is confident of the learner's understanding, while using minimal assessment doses, and such that the understanding transfers across experiences. The method implemented provides for multiple learning and assessment activities for a learning objective and leverages the same learner model for each learning dimension. Each word of a word set passes through learning and assessment-only phases for statistically significant evidence of learning.

Abstract: Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.

Abstract: An exemplary method includes obtaining a group of classification labels and corresponding confidence values for at least one object identified within an image using a computer-based object recognition technique; generating a conversation, to resolve ambiguity among the classification labels, the generating including iteratively performing the following when (i) each of the confidence values is below a threshold value or (ii) two or more of the confidence values are above the threshold value: using a wordweb to identify properties that distinguish between a first one and a second one of the labels, ranking the properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first and second label based on user input received in response to the question; and when only one of the confidence values exceeds the threshold value, classifying the object using the label corresponding to the one confidence value.

Abstract: Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: obtaining a group of classification labels and corresponding confidence values for an object in an image; using a wordweb to determine one or more properties that distinguish between at least a first one of the classification labels and at least a second one of the classification labels within the group; selecting a first property from the properties to generate a question based on information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a belief threshold value.

Abstract: Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: initiating a conversation for resolving ambiguity among a group of labels corresponding to an object in an image; using a wordweb to determine properties that distinguish between at least one first label and at least one second label within the group; selecting a first property from the properties to generate a question, wherein said selecting is based at least in part on a learner model of a user including information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a threshold value.

Abstract: A system for performing implicit assessment of a pupil is provided. The system receives a learning content from a content provider, the learning content having checkpoints at different stages of the learning content. The system associates assessment instructions with each checkpoint of the learning content. The system renders the received learning content for presentation to a pupil and captures the behaviors of the pupil during the presentation of the learning content. The system performs an assessment of the pupil at each checkpoint encountered during the rendering of the learning presentation. The assessment at a checkpoint includes using the checkpoint's associated assessment instructions to assign a score based on the captured behaviors. The system then produces an overall evaluation based on the assessments performed at the encountered checkpoints.

Abstract: An exemplary method includes initiating a conversation with a user for resolving ambiguity among a group of labels corresponding to an object in an image, wherein each label is associated with a confidence value; iteratively performing, when each of the confidence values is below a first threshold value, or two or more of the confidence values are above the first threshold value: using a wordweb to identify properties that distinguish between a first one of the labels and a second one of the labels, ranking the identified one or more properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first label and the second label based on user input received in response to the question; and when only one of the confidence values exceeds the first threshold value, classifying the object using the label corresponding to the one confidence value.

Abstract: Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: initiating a conversation for resolving ambiguity among a group of labels corresponding to an object in an image; using a wordweb to determine properties that distinguish between at least one first label and at least one second label within the group; selecting a first property from the properties to generate a question, wherein said selecting is based at least in part on a learner model of a user including information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a threshold value.

Abstract: An exemplary method includes initiating a conversation with a user for resolving ambiguity among a group of labels corresponding to an object in an image, wherein each label is associated with a confidence value; iteratively performing, when each of the confidence values is below a first threshold value, or two or more of the confidence values are above the first threshold value: using a wordweb to identify properties that distinguish between a first one of the labels and a second one of the labels, ranking the identified one or more properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first label and the second label based on user input received in response to the question; and when only one of the confidence values exceeds the first threshold value, classifying the object using the label corresponding to the one confidence value.