Abstract: A query can be received from a user. The query can be sent to a plurality of automated agents to process the query. Results and associated confidence scores can be received from the plurality of automated agents. At least some of the results and associated confidence scores can be probed, based at least on a reason given for a result having the highest associated confidence score among the received results and associated confidence scores, to select an automated agent from the plurality of automated agents for answering the query. Information can be stored, where the information can include at least the results and associated confidence scores and a selected automated agent for answering the query, where at least one of the plurality of automated agents learns from the stored information to update its confidence score in answering the query.

Abstract: A computer product and methodology is provided for optimizing publication and subscription expressiveness. A first computerized subscription presenting a first query for desired information is established. A first computerized publication having first published information is identified. A determination is made whether the first computerized publication matches the first computerized subscription. After determining a match, the first computerized publication is combined with the first computerized subscription as a first data pair. The first data pair is employed to inference a first notification having an expression of the first query's desired information.

Abstract: A computer-implemented method, a computer program product, and a computer system for classifying relevance of training data. A computer uses labeled data from users as training data to train a relevance classifier. A computer classifies, by the relevance classifier, the labeled data from the users into a set of groups. A computer generates, by the relevance classifier, relevant training data partitioned by the set of groups. In response to receiving a query from a user, a computer selects, from the relevant training data, relevant training samples for the user, where the relevant training samples are in one or more groups to which the user belongs. A computer selects, from relevant training samples for the user, top relevant training samples for the user. A computer uses the top relevant training samples for the user to generate a prompt of a machine learning model.

Abstract: Mechanisms are provided for selecting an artificial intelligence (AI) computer model for processing an input. The mechanisms generate a distribution of characteristics of previous input data processed by the data processing system. The mechanisms receive current input data and compare characteristics of the current input data to the distribution to generate a measure of similarity. An AI computer model selection engine processes the measure of similarity to select an AI computer model from a plurality of different AI computer models. The processing of the measure of similarity includes evaluation of the measure of similarity relative to one or more threshold values. The current input data is processed by the selected AI computer model to generate a result of processing the current input data.

Abstract: A method, computer system, and a computer program product for generating a conversational bot for an application programming interface (API) is provided. The present invention may include parsing an API schema. The present invention may include generating sentences for the conversational bot from the parsed API schema. The present invention may include constructing the conversational bot by training a deep learning model. The present invention may include receiving a natural language expression from a user. The present invention may include determining whether the natural language expression is enough to activate the bot.

Abstract: An example operation may include one or more of tuning a language model based on dependencies between an original data set and a paraphrase data set of the original data set, parsing and annotating the paraphrase dataset with entity identifiers of predefined entities to generate an annotated paraphrase dataset, additionally tuning the language model based on entity dependencies between the original data set and the paraphrase data set based on the annotated paraphrase dataset, and storing the additionally tuned language model in a storage device.

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: Aspects of the present disclosure relate to audio/video (A/V) stream functionality verification. A stream segment of a video feed prior to transmission over a network as captured by a transmitting device within a web-based conference can be stored. A stream segment of the video feed after transmission over the network as received by a receiving device within the web-based conference can be stored. The stream segment of the video feed prior to transmission over the network can be compared with the stream segment of the video feed after transmission over the network to determine a video feed quality.

Abstract: Embodiments of the invention are directed to a programmable computer system that includes a processor system operable to perform processor system operations. The processor system operations include using a workflow composer to perform an automated workflow composition process that generates a composed workflow that is operable to, when executed by a host device, satisfy a target logical goal. Performing the automated workflow composition process includes using a workflow-metric model to control the automated workflow composition process such that the composed workflow is operable to, when executed by the host device, satisfy the target logical goal in a manner that optimizes a target metric goal. The target metric goal quantifies a performance feature of the composed workflow.

Abstract: According to one embodiment, a method, computer system, and computer program product for software agent synthesis is provided. The present invention may include generating one or more training examples from historical data and software agents; training, using the on the one or more training examples, a language model to synthesize a software agent based on a natural language input from a user; monitoring, using one or more input devices, for one or more natural language user inputs; responsive to identifying one or more natural language user inputs, synthesizing, using the trained language model, one or more software agents based on the one or more natural language user inputs; execute the one or more new software agents to carry out one or more tasks invoked by the one or more natural language user inputs.

Abstract: An embodiment for identifying and processing poly-process natural language queries may include receiving a natural language query. The embodiment may also automatically identify a bridge entity in the received natural language query. The embodiment may also automatically determine whether the received natural language query is a poly-process query. The embodiment may further include, in response to identifying that the received natural language query is the poly-process query, automatically generating sub-queries for each process in the poly-process query and generate results for each sub-query. The embodiment may also automatically combining the results of each sub-query using the bridge entity to output a combined result. The embodiment may further include automatically generating a modified sub-query for post-processing of the combined result. The embodiment may also automatically process the modified sub-query to generate a final query result for the received natural language query.

Abstract: A method, computer system, and a computer program product for anomaly detection is provided. The present invention may include converting business process logs into a graphical data structure. The present invention may include generating an optimized graph encoding for anomaly detection using an unsupervised machine learning model. The present invention may include computing an anomaly score for each activity of the business process log using a process aware metric based on feature representation. The present invention may include labeling each of the one or more data points with a high anomaly score.

Abstract: Embodiments of the invention include a computer-implemented method that uses a processor system to access a first machine learning (ML) model. The first ML model has been trained using data of a first server. A first performance metric of the first ML model is determined using data of a second server. A benefit analysis is performed to determine a benefit of the first ML server and the second ML server participating in a federated learning system, where the benefit analysis includes using the first performance metric.

Abstract: An embodiment including a domain-agnostic natural language processing system for processing natural language queries having an explainable interpretation feedback model is provided. The embodiment may include receiving a natural language query. The embodiment may also include to automatically detecting whether the received natural language query includes implicit intent therein. The embodiment may include, in response to detecting implicit intent in the received natural language query, automatically generating a modified query including a default inference from an interpretation fact sheet. The embodiment may further include automatically presenting the modified query to the user and asking the user for feedback on the modified query.

Abstract: A method, computer system, and a computer program product for anomaly detection is provided. The present invention may include converting business process logs into a graphical data structure. The present invention may include generating an optimized graph encoding for anomaly detection using an unsupervised machine learning model. The present invention may include computing an anomaly score for each activity of the business process log using a process aware metric based on feature representation. The present invention may include labeling each of the one or more data points with a high anomaly score.

Abstract: An embodiment for identifying and processing poly-process natural language queries may include receiving a natural language query. The embodiment may also automatically identify a bridge entity in the received natural language query. The embodiment may also automatically determine whether the received natural language query is a poly-process query. The embodiment may further include, in response to identifying that the received natural language query is the poly-process query, automatically generating sub-queries for each process in the poly-process query and generate results for each sub-query. The embodiment may also automatically combining the results of each sub-query using the bridge entity to output a combined result. The embodiment may further include automatically generating a modified sub-query for post-processing of the combined result. The embodiment may also automatically process the modified sub-query to generate a final query result for the received natural language query.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate model transfer learning across evolving processes 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 condition definition component that defines one or more conditions associated with use of a model trained on first traces of a first process to make a prediction on one or more second traces of a second process. The computer executable components can further comprise a guardrail component that determines whether to use the model to make the prediction.

Abstract: Aspects of the present disclosure relate to audio/video (A/V) stream functionality verification. A stream segment of a video feed prior to transmission over a network as captured by a transmitting device within a web-based conference can be stored. A stream segment of the video feed after transmission over the network as received by a receiving device within the web-based conference can be stored. The stream segment of the video feed prior to transmission over the network can be compared with the stream segment of the video feed after transmission over the network to determine a video feed quality.

Abstract: A method, computer system, and a computer program product for generating a conversational bot for an application programming interface (API)is provided. The present invention may include parsing an API schema. The present invention may include generating sentences for the conversational bot from the parsed API schema. The present invention may include constructing the conversational bot by training a deep learning model. The present invention may include receiving a natural language expression from a user. The present invention may include determining whether the natural language expression is enough to activate the bot.

Abstract: A query can be received from a user. The query can be sent to a plurality of automated agents to process the query. Results and associated confidence scores can be received from the plurality of automated agents. At least some of the results and associated confidence scores can be probed, based at least on a reason given for a result having the highest associated confidence score among the received results and associated confidence scores, to select an automated agent from the plurality of automated agents for answering the query. Information can be stored, where the information can include at least the results and associated confidence scores and a selected automated agent for answering the query, where at least one of the plurality of automated agents learns from the stored information to update its confidence score in answering the query.