Abstract: Common data are maintained by a system including a plurality of node devices that each store a respective portion of a common data in a respective database, wherein a node device of the plurality of node devices stores a first portion of the common data, and receives a hash value of a second portion of the common data that is different from the first portion, in response to an update of the second portion of the common data.

Abstract: A computer-implemented method is provided for generating a new formulation. The method includes dividing each of input formulations into constituent topics, based on analysis results for an analysis of the input formulations using a topic model algorithm. The method further includes receiving an input query that specifies a set of fragrance notes to be used to generate the new formulation. The method also includes choosing one of the input formulations which includes the set of fragrance notes to be used to generate the new formulation. The method additionally includes clustering the constituent topics of the chosen one of the input formulations based on a similarity metric. The method further includes generating the new formulation as a response to the input query by selecting, from the input formulations, materials for each of the clustered ones of the constituent topics.

Abstract: A computer-implemented method for producing a formulation based on a prior distribution of a number of ingredients used in the formulation includes grouping a set of energy functions based on a number of ingredients used in a formulation, generating a probability distribution using the set of energy functions, obtaining at least one sample of the formulation by sampling from the probability distribution based on a previous sample, and triggering fabrication of the formulation in accordance with the at least one sample.

Abstract: Embodiments are directed to a computer implemented method of analyzing a graph having a plurality of vertices and edges to identify a subgraph of the graph, wherein the subgraph includes an exact subset. The method includes applying, by a first processor, a heuristic analysis to the graph to determine an initial subset of the graph. The method further includes applying, by either the first processor or a second processor, an exact analysis to the initial subset to determine an exact subset of the initial subset.

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 non-transitory computer readable storage medium having instructions embodied therewith, the instructions executable by a processor or programmable circuitry to cause the processor or programmable circuitry to perform operations including obtaining training data including a sample value of one or more input features of an item and a sample value of an output feature representing demand for the item, and training, based on the training data, an estimation model that estimates a new value of the output feature for the item based on new values of the one or more input features. The one or more input features may include a relative price of the item relative to prices of a plurality of items.

Abstract: A method includes hierarchically identifying occurrences of some of the words in the set of sentences; creating a first index for each of some of the words based on the upper hierarchy of occurrences identified for each word; receiving input of a queried word; hierarchically identifying occurrences of the queried word in the set of sentences; creating a second index based on the upper hierarchy of occurrences identified for the queried word; comparing the first index and the second index to calculate an estimated value for the number of occurrences of a word in the neighborhood of the queried word; and calculating the actual value of the number of occurrences of a word in the neighborhood of the queried word based on an upper hierarchy and lower hierarchy of the occurrences on condition that the estimated value is equal to or greater than a predetermined number.

Abstract: A method includes obtaining a plurality of clauses associated with a plurality of logical variables, each of the clauses consisting of a weight and a disjunction of one or more literals of the logical variables, detecting conditions associated with one or more inference rules, and simplifying the plurality of clauses on the basis of the detecting.

Abstract: A method, apparatus and computer program product for solving a mixed integer programming problem. The apparatus includes a generating section configured to generate a relaxed mixed integer programming problem by relaxing each of only a part of integer variables of the mixed integer programming problem to a continuous variable, a solver configured to solve the relaxed mixed integer programming problem, and a determining section configured to determine, using a processor, a feasible solution of the mixed integer programming problem based on a solution of the relaxed mixed integer programming problem.

Abstract: A method, apparatus and computer program product for solving a mixed integer programming problem. The apparatus includes a generating section configured to generate a relaxed mixed integer programming problem by relaxing each of only a part of integer variables of the mixed integer programming problem to a continuous variable, a solver configured to solve the relaxed mixed integer programming problem, and a determining section configured to determine, using a processor, a feasible solution of the mixed integer programming problem based on a solution of the relaxed mixed integer programming problem.

Abstract: Common data are maintained by a system including a plurality of node devices that each store a respective portion of a common data in a respective database, wherein a node device of the plurality of node devices stores a first portion of the common data, and receives a hash value of a second portion of the common data that is different from the first portion, in response to an update of the second portion of the common data.

Abstract: Common data are maintained by a system including a plurality of node devices that each store a respective portion of a common data in a respective database, wherein a node device of the plurality of node devices stores a first portion of the common data, and receives a hash value of a second portion of the common data that is different from the first portion, in response to an update of the second portion of the common data.

Abstract: A method is provided for determining graph isomorphism. The method includes initializing a hash value for each of a plurality of vertexes in a first labelled graph and a second labelled graph by assigning an integer value as the hash value, to form a first set of hash values for the vertexes in the first graph and a second set of hash values for the vertexes in the second graph. The integer value for a given vertex is assigned based on a label of the given vertex in the graphs. The method includes performing a determination of whether the first and second labelled graphs are isomorphic, by comparing the first and second sets of hash values. The method includes initiating a performance of an action that changes a state of a controlled object to another state, responsive to the determination. Each graph includes a mixture of hard and soft labels.

Abstract: A method is provided for determining graph isomorphism. The method includes initializing a hash value for each of a plurality of vertexes in a first labelled graph and a second labelled graph by assigning an integer value as the hash value, to form a first set of hash values for the vertexes in the first graph and a second set of hash values for the vertexes in the second graph. The integer value for a given vertex is assigned based on a label of the given vertex in the graphs. The method includes performing a determination of whether the first and second labelled graphs are isomorphic, by comparing the first and second sets of hash values. The method includes initiating a performance of an action that changes a state of a controlled object to another state, responsive to the determination.

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: A method, apparatus and computer program product for solving a mixed integer programming problem. The apparatus includes a generating section configured to generate a relaxed mixed integer programming problem by relaxing each of only a part of integer variables of the mixed integer programming problem to a continuous variable, a solver configured to solve the relaxed mixed integer programming problem, and a determining section configured to determine, using a processor, a feasible solution of the mixed integer programming problem based on a solution of the relaxed mixed integer programming problem.

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.

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.

Abstract: A non-transitory computer readable storage medium having instructions embodied therewith, the instructions executable by a processor or programmable circuitry to cause the processor or programmable circuitry to perform operations including obtaining training data including a sample value of one or more input features of an item and a sample value of an output feature representing demand for the item, and training, based on the training data, an estimation model that estimates a new value of the output feature for the item based on new values of the one or more input features. The one or more input features may include a relative price of the item relative to prices of a plurality of items.