Abstract: A computer-implemented method for constructing and searching structured data of question sentences in a question-answer database using machine learning and natural language processing includes receiving one or more question-answer articles as input from one or more question-answer databases over at least one network, extracting one or more profile keywords from a profile associated with a user, assigning a personalization score corresponding to the user to each of the one or more question-answer articles based on the one or more profile keywords, including applying a morphological and dependency analysis to a body section of the question-answer article, receiving a query for obtaining question-answer data relevant to the user, and outputting question-answer data based at least in part on the personalization score of each question-answer article in satisfaction of the query.

Abstract: A method is provided for rule creation that includes receiving (i) a MDP model with a set of states, a set of actions, and a set of transition probabilities, (ii) a policy that corresponds to rules for a rule engine, and (iii) a set of candidate states that can be added to the set of states. The method includes transforming the MDP model to include a reward function using an inverse reinforcement learning process on the MDP model and on the policy. The method includes finding a state from the candidate states, and generating a refined MDP model with the reward function by updating the transition probabilities related to the state. The method includes obtaining an optimal policy for the refined MDP model with the reward function, based on the reward policy, the state, and the updated probabilities. The method includes updating the rule engine based on the optimal policy.

Abstract: A method is provided for anonymizing statistical data for a secure transfer. The method calculates statistical information for each of the statistical data. The method aggregates the statistical information to calculate a valid range for each of the statistical information. The method removes outlier data based on the valid range for each of the statistical data. The method creates pair lists from each of the statistical data and target data, the pair lists having a respective member from both the statistical data and the target data. The method replaces each respective member of the target data by a random number existing in a range of a corresponding one of a plurality of target data bins. The method swaps each pair in each pair list in a random order using the randomized number, wherein the random number used for swapping is different for different ones of the pair lists.

Abstract: A computer-implemented method is presented for building a topic model to discover topics in a collection of documents generated by a plurality of users. The method includes extracting conversations from the collection of documents, dividing the extracted conversations into a plurality of segments, generating a topic distribution for each of the plurality of segments based on the extracted conversations and a first pre-defined prior probability distribution, and generating continuous value constructs for each of the topic distributions based on an external corpus and a second pre-defined prior probability distribution, wherein similarity is defined between the continuous value constructs.

Abstract: A computer-implemented method is provided for machine prediction. The method includes forming, by a hardware processor, a Convolutional Dynamic Boltzmann Machine (C-DyBM) by extending a non-convolutional DyBM with a convolutional operation. The method further includes generating, by the hardware processor using the convolution operation of the C-DyBM, a prediction of a future event at time t from a past patch of time-series of observations. The method also includes performing, by the hardware processor, a physical action responsive to the prediction of the future event at time t.

Abstract: A computer-implemented method for constructing and searching structured data of question sentences in a question-answer database using machine learning and natural language processing includes receiving one or more question-answer articles as input from one or more question-answer databases over at least one network, extracting one or more profile keywords from a profile associated with a user, assigning a personalization score corresponding to the user to each of the one or more question-answer articles based on the one or more profile keywords, including applying a morphological and dependency analysis to a body section of the question-answer article, receiving a query for obtaining question-answer data relevant to the user, and outputting question-answer data based at least in part on the personalization score of each question-answer article in satisfaction of the query.

Abstract: A method for implementing artificial intelligence agents to perform machine learning tasks using predictive analytics to leverage ensemble policies for maximizing long-term returns includes obtaining a set of inputs including a set of ensemble policies and a meta-policy parameter, selecting an action for execution within the system environment using a meta-policy function determined based in part on the set of ensemble policies and the meta-policy function parameter, causing the artificial intelligence agent to execute the selected action within the system environment, and updating the meta-policy function parameter based on the execution of the selected action.

Abstract: A method, a system, and a computer program product provide for changing a boundary between zones. This includes setting a location of a boundary which defines a part of a zone; detecting that an object moves into the zone across the boundary; and in response to the detection, changing the location of the boundary in a direction away from the object, compared to the location of the boundary before the change.

Abstract: A method, a system, and a computer program product provide for changing a boundary between zones. This includes setting a location of a boundary which defines a part of a zone; detecting that an object moves into the zone across the boundary; and in response to the detection, changing the location of the boundary in a direction away from the object, compared to the location of the boundary before the change.

Abstract: A method, computer system, and a computer program product for conducting forward reasoning is provided. The present invention may include conducting the forward reasoning, wherein the forward reasoning includes selecting a rule from a plurality of rules stored in a rule base and executing an action, wherein the rule is associated with a condition satisfied by internal states stored in a working memory, and wherein the action is associated with the condition. The present invention may also include detecting the action is creating a one-time object. The present invention may then include conducting the forward reasoning with a first new context in response to the detected one-time object, wherein the one-time object is stored as one of the internal states in the working memory. The present invention may further include deleting the one-time object in response to a completion of the forward reasoning with the first new context.

Abstract: A method of collecting data into a server from multiple client computers is provided as a first aspect of the invention. The method includes the steps of: reconstructing a collection network in a tree structure having the server acting as a highest node and the multiple client computers acting as child nodes every time the data is collected; the server broadcasting a parent-child declaration to nodes placed at the lower level; and each of the nodes transferring data to a node placed at the higher level.

Abstract: A method, computer system, and a computer program product for conducting forward reasoning is provided. The present invention may include conducting the forward reasoning, wherein the forward reasoning includes selecting a rule from a plurality of rules stored in a rule base and executing an action, wherein the rule is associated with a condition satisfied by internal states stored in a working memory, and wherein the action is associated with the condition. The present invention may also include detecting the action is creating a one-time object. The present invention may then include conducting the forward reasoning with a first new context in response to the detected one-time object, wherein the one-time object is stored as one of the internal states in the working memory. The present invention may further include deleting the one-time object in response to a completion of the forward reasoning with the first new context.

Abstract: A method is provided for rule creation that includes receiving (i) a MDP model with a set of states, a set of actions, and a set of transition probabilities, (ii) a policy that corresponds to rules for a rule engine, and (iii) a set of candidate states that can be added to the set of states. The method includes transforming the MDP model to include a reward function using an inverse reinforcement learning process on the MDP model and on the policy. The method includes finding a state from the candidate states, and generating a refined MDP model with the reward function by updating the transition probabilities related to the state. The method includes obtaining an optimal policy for the refined MDP model with the reward function, based on the reward policy, the state, and the updated probabilities. The method includes updating the rule engine based on the optimal policy.

Abstract: A method is provided for rule creation that includes receiving (i) a MDP model with a set of states, a set of actions, and a set of transition probabilities, (ii) a policy that corresponds to rules for a rule engine, and (iii) a set of candidate states that can be added to the set of states. The method includes transforming the MDP model to include a reward function using an inverse reinforcement learning process on the MDP model and on the policy. The method includes finding a state from the candidate states, and generating a refined MDP model with the reward function by updating the transition probabilities related to the state. The method includes obtaining an optimal policy for the refined MDP model with the reward function, based on the reward policy, the state, and the updated probabilities. The method includes updating the rule engine based on the optimal policy.

Abstract: An extraction method for extracting a sub query to be converted to a program for processing stream data continuously inputted to a database, from a query including instructions, as sub queries, to be issued to a database management system. The extraction method includes receiving input of the query and a lower limit value of efficiency as processing time per unit memory increase amount. A calculating operation calculates a one memory increase amount and the efficiency using the memory increase amount and the processing time to be reduced. The method selects a sub query whose calculated efficiency is equal to or higher than the lower limit value and extracts the selected sub query as a conversion object on condition that the integrated memory increase amount is equal to or smaller than the maximum memory increase amount.

Abstract: A method, a system, and a computer program product provide for changing a boundary between zones. This includes setting a location of a boundary which defines a part of a zone; detecting that an object moves into the zone across the boundary; and in response to the detection, changing the location of the boundary in a direction away from the object, compared to the location of the boundary before the change.

Abstract: A method, a system, and a computer program product provide for changing a boundary between zones. This includes setting a location of a boundary which defines a part of a zone; detecting that an object moves into the zone across the boundary; and in response to the detection, changing the location of the boundary in a direction away from the object, compared to the location of the boundary before the change.

Abstract: A computer-implemented method for learning a policy for selection of an associative topic, which can be used in a dialog system, is described. The method includes obtaining a policy base that indicates a topic transition from a source topic to a destination topic and a short-term reward for the topic transition, by analyzing data from a corpus. The short-term reward may be defined as probability of associating a positive response. The method also includes calculating an expected long-term reward for the topic transition using the short-term reward for the topic transition with taking into account a discounted reward for a subsequent topic transition. The method further includes generating a policy using the policy base and the expected long-term reward for the topic transition. The policy indicates selection of the destination topic for the source topic as an associative topic for a current topic.

Abstract: A computer-implemented method for learning a policy for selection of an associative topic, which can be used in a dialog system, is described. The method includes obtaining a policy base that indicates a topic transition from a source topic to a destination topic and a short-term reward for the topic transition, by analyzing data from a corpus. The short-term reward may be defined as probability of associating a positive response. The method also includes calculating an expected long-term reward for the topic transition using the short-term reward for the topic transition with taking into account a discounted reward for a subsequent topic transition. The method further includes generating a policy using the policy base and the expected long-term reward for the topic transition. The policy indicates selection of the destination topic for the source topic as an associative topic for a current topic.

Abstract: A method for selecting an action, includes reading, into a memory, a Partially Observed Markov Decision Process (POMDP) model, the POMDP model having top-k action IDs for each belief state, the top-k action IDs maximizing expected long-term cumulative rewards in each time-step, and k being an integer of two or more, in the execution-time process of the POMDP model, detecting a situation where an action identified by the best action ID among the top-k action IDs for a current belief state is unable to be selected due to a constraint, and selecting and executing an action identified by the second best action ID among the top-k action IDs for the current belief state in response to a detection of the situation. The top-k action IDs may be top-k alpha vectors, each of the top-k alpha vectors having an associated action, or identifiers of top-k actions associated with alpha vectors.