Abstract: A computer-implemented method includes determining a current state and a current context of an environment in which an automated agent runs to execute a contingent plan. The state indicates that one or more fluents of a plurality of fluents are true, and the plurality of fluents are associated with a contingent problem solved by the contingent plan. The context describes values corresponding to the one or more fluents. An action is performed with respect to at least a subset of the context. A nondeterministic effect of the action on the environment is evaluated, using a computer processor. The state is updated based on the nondeterministic effect. The context is updated based on the nondeterministic effect.

Abstract: Embodiments herein describe an automated process for identifying tasks that can be converted from primarily being driven by a human agent into bot-driven RPAs. This disclosure describes techniques for automatically generating RPAs by monitoring the actions of the agent and identifying corresponding events. These events can be performed on unstructured data sources and structured data sources. The events are clustered into different tasks where pattern detection techniques are used to identify common (or overlapping) events and causal relationships between the events. Using these causal relationships, the system can map events performed on unstructured data sources to events performed on structured data source. The system can then generate respective bot-RPAs for the tasks. The system can monitor, in real-time, data received from unstructured and/or structured data sources and when it identifies data for one of the bot-RPAs, automatically execute the RPA to perform the task.

Abstract: Embodiments relate to personalized low latency communications. A method may include receiving a description of content of a message, receiving recipient data corresponding to at least two possible recipients within a population of possible recipients, and selecting a relevant subpopulation of the population. The selecting may include, for each of the at least two possible recipients, ranking a strength of an indirect relationship between the description and the recipient data. The indirect relationship may be based on the description, the recipient data and at least one additional data source. The selecting may also include, for each of the at least two possible recipients, adding a possible recipient to the relevant subpopulation based on the ranking of the indirect relationship associated with the possible recipient. The method may further include initiating a two-way communication channel between a sender of the message and the relevant subpopulation.

Abstract: A method and apparatus are provided for recommending concepts from a first concept set in response to user selection of a first concept Ci by performing a natural language processing (NLP) analysis comparison of vector representations of user concepts contained in written content authored by the user and candidate concepts in a first concept set to determine a similarity measure for each candidate concept, and to select therefrom one or more of the candidate concepts for display as recommended concepts which are related to the user concepts contained in written content authored by the user based on the similarity measure between each candidate concept and each user concept.

Abstract: A domain-specific document is retrieved. The domain-specific document is related to the performance of a process in the domain and includes a plurality of document spans and a plurality of annotations that annotate the spans. A goal-based framework is generated from the domain-specific document. The generation is based on the plurality of annotations and based on the plurality of document spans. The goal-based framework includes entities and entity relationships related to performance of the process. The goal-based framework is transformed into a platform agnostic dialog model based on the entities and the entity relationships. At least one platform specific dialog model is provided to at least one automated response system based on the platform agnostic dialog model.

Abstract: Embodiments relate to personalized low latency communications. A method may include receiving a description of content of a message, receiving recipient data corresponding to at least two possible recipients within a population of possible recipients, and selecting a relevant subpopulation of the population. The selecting may include, for each of the at least two possible recipients, ranking a strength of an indirect relationship between the description and the recipient data. The indirect relationship may be based on the description, the recipient data and at least one additional data source. The selecting may also include, for each of the at least two possible recipients, adding a possible recipient to the relevant subpopulation based on the ranking of the indirect relationship associated with the possible recipient. The method may further include initiating a two-way communication channel between a sender of the message and the relevant subpopulation.

Abstract: A domain-specific document is retrieved. The domain-specific document is related to the performance of a process in the domain and includes a plurality of document spans and a plurality of annotations that annotate the spans. A goal-based framework is generated from the domain-specific document. The generation is based on the plurality of annotations and based on the plurality of document spans. The goal-based framework includes entities and entity relationships related to performance of the process. The goal-based framework is transformed into a platform agnostic dialog model based on the entities and the entity relationships. At least one platform specific dialog model is provided to at least one automated response system based on the platform agnostic dialog model.

Abstract: According to one aspect of the present disclosure a method and technique for managing data transfer includes receiving and storing a plurality of different data patterns anticipated to be encountered by a processor unit of a data processing system corresponding to a particular application being processed. Responsive to receiving a read request for data, the requested data is read from a memory subsystem, and the read data is compared by the memory subsystem to the stored data patterns. Responsive to determining that the read data matches at least one of the stored data patterns, the memory subsystem replaces the matching read data with a pattern tag corresponding to the matching data pattern. The pattern tag is transmitted over a communication link in response to the request.

Abstract: A computer-implemented method identifies one or more concepts from a document, wherein the document comprises information on completing a task. The method computes a relevance value of an utterance with respect to completing the task using the one or more identified concepts. The method removes the utterance from a dialog model to be used for completing the task when the relevance value of the utterance is below a given threshold value.

Abstract: An approach is provided for automatically generating and processing concept vectors by extracting concept sequences from one or more content sources and generating a first concept vector for a first concept by supplying the concept sequences as inputs to a vector learning component, such that the first concept vector comprises information interrelating the first concept to other concepts in the concept sequences which is inferred from the concept sequences.

Abstract: Generating, updating, and using a knowledge graph. Concepts in a knowledge graph can have relations to one another. These relations may be expressed as confidence values. A training data set may be split into two portions, with the first portion used to update confidence values for existing relations between concept pairs, using the knowledge graph. These confidence values can be used, together with the second portion used to update confidence values for known phrases that express known relations. These confidence values, in turn, can be used, together with the first portion, to increase the accuracy of the original confidence scores with respect to existing relations. The process may be iteratively employed, with each iteration increasing the accuracy of confidence scores.

Abstract: A time of day (TOD) synchronizer in a first processor transmits a latency measure message simultaneously on two links to a second processor. In response, the receiver in the second processor detects latency differential between the two links, detects the delay in the second processor, and sends the latency differential and delay to the first processor on one of the two links. The first processor stores TOD delay values in the two links that account for the latency differential between the two links. When a TOD message needs to be sent, a link loads a counter with its stored TOD delay value, then decrements the counter until the TOD message is ready to be sent. The resulting counter value is the receiver delay value, which is transmitted to the receiver as data in the TOD message, thereby reducing TOD jitter between the two links.

Abstract: Mechanisms, in a system comprising a host system and at least one accelerator device, for performing a concept analysis operation are provided. The host system extracts a set of one or more concepts from an information source and provides the set of one or more concepts to the accelerator device. The host system also provides at least one matrix representation data structure representing a graph of concepts and relationships between concepts in a corpus. The accelerator device executes the concept analysis operation internal to the accelerator device to generate an output vector identifying concepts in the corpus, identified in the at least one matrix representation data structure, related to the set of one or more concepts extracted from the information source. The accelerator device outputs the output vector to the host system which utilizes the output vector to respond to a request submitted to the host system associated with the information source.

Abstract: According to an aspect, searching, recommending, and exploring documents through conceptual associations includes a method for receiving a plurality of documents and extracting concepts from each of the documents. A degree of relation between each of the documents and concepts in a knowledge base is calculated. The method also includes, in response to receiving a query, determining one or more concepts from the query. For each of the concepts, a list of documents having a highest degree of relation to the concept is retrieved. The method also includes outputting a list that is responsive to the one or more retrieved lists.

Abstract: Generating, updating, and using a knowledge graph. Concepts in a knowledge graph can have relations to one another. These relations may be expressed as confidence values. A training data set may be split into two portions, with the first portion used to update confidence values for existing relations between concept pairs, using the knowledge graph. These confidence values can be used, together with the second portion used to update confidence values for known phrases that express known relations. These confidence values, in turn, can be used, together with the first portion, to increase the accuracy of the original confidence scores with respect to existing relations. The process may be iteratively employed, with each iteration increasing the accuracy of confidence scores.

Abstract: A method and apparatus are provided for automatically analyzing candidate concepts extracted from a first source text against a reference concept set comprising a plurality of concepts by obtaining a vector representation for each of the concepts in the first concept set and the reference concept set and performing a natural language processing (NLP) analysis comparison of the candidate concepts to the reference concept set to determine a similarity measure corresponding to each candidate concept and validating one or more of the candidate concepts based on the similarity measure for each candidate concept meeting a minimum similarity threshold requirement.

Abstract: A method and apparatus are provided for automatically generating and processing first and second concept vector sets extracted, respectively, from a first set of concept sequences and from a second, temporally separated, concept sequences by performing a natural language processing (NLP) analysis of the first concept vector set and second concept vector set to detect changes in the corpus over time by identifying changes for one or more concepts included in the first and/or second set of concept sequences.

Abstract: A mechanism is provided for search engine domain transfer. The mechanism receives an input query to search a specialized domain from a user and performs a general-domain search based on the input query to generate a set of general-domain results. The mechanism generates a feature vector based on the general-domain results and generates a score for each document within the specialized domain based on the feature vector. The mechanism generates a ranked result set of documents based on the scores of the documents in the specialized domain and presents the ranked result set to the user.

Abstract: A mechanism is provided for search engine domain transfer. The mechanism receives an input query to search a specialized domain from a user and performs a general-domain search based on the input query to generate a set of general-domain results. The mechanism generates a feature vector based on the general-domain results and generates a score for each document within the specialized domain based on the feature vector. The mechanism generates a ranked result set of documents based on the scores of the documents in the specialized domain and presents the ranked result set to the user.

Abstract: A method and apparatus are provided for automatically generating and processing first and second concept vector sets extracted, respectively, from a first set of concept sequences and from a second, temporally separated, concept sequences by performing a natural language processing (NLP) analysis of the first concept vector set and second concept vector set to detect changes in the corpus over time by identifying changes for one or more concepts included in the first and/or second set of concept sequences.