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.

Abstract: An assistance strategy may be generated with a generating apparatus including a processor, and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to create a reward estimation model for estimating a reward for assisting at least one subject by analyzing a history of input by the subject, create a decision making model including a plurality of forms of assistance and estimated rewards for each form of assistance based on the reward estimation model and the history of input by the subject, and generate an assistance strategy based on the decision making model.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. The method includes the steps of: inputting measurement data having an anomalous or normal label and measurement data having no label as samples; determining a similarity matrix indicating the relationship between the samples based on the samples; defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty; and calculating a degree of anomaly based on the calculated parameters. The present invention also provides a program and system for detecting an anomaly based on measurement data.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. The method includes the steps of: inputting measurement data having an anomalous or normal label and measurement data having no label as samples; determining a similarity matrix indicating the relationship between the samples based on the samples; defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty; and calculating a degree of anomaly based on the calculated parameters. The present invention also provides a program and system for detecting an anomaly based on measurement data.

Abstract: A battery controller and method for controlling a battery include training parameters for a battery capacity prediction model based on usage pattern information that correlates usage of similar batteries with capacity information for the respective similar batteries. The model characterizes a capacity decay rate in accordance with a present value of the battery capacity. Future battery capacity is predicted for a battery under control based on the battery capacity prediction model and a present value of the battery capacity. One or more operational parameters of the battery under control are controlled based on the predicted future battery capacity to extend the battery's usable lifetime.

Abstract: Methods and systems are provided for obtaining cleaned sequences showing trajectories of movement of a center of gravity and for estimating a biometric information pattern or value of a target. One of the methods includes removing noises from initial sequences showing trajectories of movement of a center of gravity to obtain the cleaned sequences. Another one of the methods includes reading cleaned sequences of the target into a memory, extracting features from the cleaned sequences, and estimating a biometric information pattern or value of the target from the extracted features, using a classification or regression model of biometric information patterns or values. The biometric information pattern may be a pattern derived from respiratory or circulatory organs of a target.

Abstract: A battery controller and method for controlling a battery include training parameters for a battery capacity prediction model based usage of similar batteries and capacity information for the respective similar batteries. The model characterizes a capacity decay rate. Future battery capacity is predicted for a battery under control based on the battery capacity prediction model and a present value of the battery capacity. One or more operational parameters of the battery under control is controlled based on the predicted future battery capacity.

Abstract: Optimized learning settings of neural networks are efficiently determined by an apparatus including a processor and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to train a first neural network with a learning setting; extract tentative weight data from the first neural network with the learning setting; calculate an evaluation value of the first neural network with the learning setting; and generate a predictive model for predicting an evaluation value of a second neural network with a new setting based on tentative weight data of the second neural network by using a relationship between the tentative weight data of the first neural network and the evaluation value of the first neural network.

Abstract: The method generating a point of sale data matrix from a purchase record, the point of sale data matrix including buying information of each item and purchase quantity information of each item, extracting purchase patterns from the point of sale data matrix using a matrix factorization method to provide a maximum score value, the maximum score value representing the purchase patterns, identifying related factors associated with each of the purchase patterns to provide a related factor model, and testing at least one simulated related factor in a simulated environment based on the related factor model to determine a difference in purchase patterns.

Abstract: An analysis device which analyzes a system that inputs input data including a plurality of input parameters and outputs output data, including an acquisition unit that acquires learning data including a plurality of sets of the input data and the output data, and a learning processing unit that learns, based on the acquired learning data, the amount of difference of output data corresponding to a difference between input parameters of two pieces of input data, an analysis method using the analysis device, and a program used in the analysis device are provided.

Abstract: A method, apparatus and computer program for detecting occurrence of an anomaly. The method can exclude arbitrariness and objectively judge whether a variation of a physical quantity to be detected is abnormal or not even when an external environment is fluctuating. The method includes acquiring multiple primary measurement values from a measurement target. Further, calculating and a reference value for each of the multiple primary measurement values by optimal learning. The method further includes calculating a relationship matrix which indicates mutual relationships between the multiple secondary measurement values. Further the method includes calculating an anomaly score for each of the secondary measurement value which indicates the degree of the measurement target being abnormal. The anomaly score is calculated by comparing the secondary measurement value with a predictive value which is calculated based on the relationship matrix and other secondary measurement values.

Abstract: An analysis device which analyzes a system that inputs input data including a plurality of input parameters and outputs output data, including an acquisition unit that acquires learning data including a plurality of sets of the input data and the output data, and a learning processing unit that learns, based on the acquired learning data, the amount of difference of output data corresponding to a difference between input parameters of two pieces of input data, an analysis method using the analysis device, and a program used in the analysis device are provided.

Abstract: A battery controller and method for controlling a battery include training parameters for a battery capacity prediction model based on usage pattern information that correlates usage of similar batteries with capacity information for the respective similar batteries. The model characterizes a capacity decay rate in accordance with a present value of the battery capacity. Future battery capacity is predicted for a battery under control based on the battery capacity prediction model and a present value of the battery capacity. One or more operational parameters of the battery under control are controlled based on the predicted future battery capacity to extend the battery's usable lifetime.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. Effective utilization of label information is based on introducing the degree of similarity between samples. Assuming, for example, there is a degree of similarity between normally labeled samples and no similarity between normally labeled and abnormally labeled samples. Also each sensor value is generated by the linear sum of a latent variable and a coefficient vector specific to each sensor. However, the magnitude of observation noise is formulated to vary according to the label information for the sensor values, and set so that normal label unlabeled anomalously labeled. A graph Laplacian is created based on the degree of similarity between samples, and determines the optimal linear transformation matrix according to a gradient method. A optimal linear transformation matrix is used to calculate an anomaly score for each sensor in the test samples.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. Effective utilization of label information is based on introducing the degree of similarity between samples. Assuming, for example, there is a degree of similarity between normally labeled samples and no similarity between normally labeled and abnormally labeled samples. Also each sensor value is generated by the linear sum of a latent variable and a coefficient vector specific to each sensor. However, the magnitude of observation noise is formulated to vary according to the label information for the sensor values, and set so that normal label?unlabeled?anomalously labeled. A graph Laplacian is created based on the degree of similarity between samples, and determines the optimal linear transformation matrix according to a gradient method. A optimal linear transformation matrix is used to calculate an anomaly score for each sensor in the test samples.

Abstract: Embodiments of the present invention specify a group of sensors detecting normal operation and a group of sensors detecting abnormal operation from time-series data on the sensors without using threshold values that are based on the experience of users. One aspect of an embodiment is a detecting device for detecting changes in the output of a plurality of sensors, in which the detecting device includes: a first output acquiring unit for acquiring a first relevance matrix; a second output acquiring unit for acquiring a second relevance matrix; a change calculating unit for calculating a change matrix representing the degree of change between the first relevance matrix and the second relevance matrix; and a specifying unit for specifying a group of sensors showing a degree of change greater than the others in the change matrix. Other aspects of the present invention include a detecting method and a program.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. The method includes the steps of: inputting measurement data having an anomalous or normal label and measurement data having no label as samples; determining a similarity matrix indicating the relationship between the samples based on the samples; defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty; and calculating a degree of anomaly based on the calculated parameters. The present invention also provides a program and system for detecting an anomaly based on measurement data.

Abstract: A method providing an analytical technique introducing label information into an anomaly detection model. The method includes the steps of: inputting measurement data having an anomalous or normal label and measurement data having no label as samples; determining a similarity matrix indicating the relationship between the samples based on the samples; defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty; and calculating a degree of anomaly based on the calculated parameters. The present invention also provides a program and system for detecting an anomaly based on measurement data.

Abstract: A processing apparatus is disclosed for representing cognitively biased selection behavior of a consumer as a learnable model with high prediction accuracy taking into account even feature values of a product and the consumer. The processing apparatus generates a selection model obtained by modeling selection behavior of a selection entity that selects at least one choice out of presented input choices. The processing apparatus includes an acquiring unit to acquire training data including a plurality of input feature vectors that indicate features of a plurality of the choices presented to the selection entity and an output feature vector that indicates a feature of an output choice. The processing apparatus further includes an input combining unit to combine the plurality of input vectors to generate an input combined vector, and a learning processing unit to learn a selection model on the basis of the input combined vector and the output vector.