Abstract: Provided is an apparatus comprising a processor and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to: acquire a candidate for a solution of an optimization problem for optimizing a third objective function based on a first objective function and a second objective function; obtain, as another candidate for the solution of the optimization problem, a solution that optimizes the second objective function under a constraint corresponding to a value of the first objective function for the acquired candidate; and select the solution of the optimization problem from among the plurality of candidates for the solution of the optimization problem. Also provided as the first aspect are a method and non-transitory computer readable storage medium.

Abstract: A computer-implemented method of predicting a risk of an accident is disclosed. The method includes computing an anomaly score based on sensor data to obtain a series of anomaly scores. The method also includes processing the anomaly score to limit a processed anomaly score below a predetermined value. The method further includes calculating an accident risk score at time of prediction by using a series of processed anomaly scores up to the time of the prediction. The method includes further outputting a prediction result based on the accident risk score.

Abstract: A computer-implemented method of predicting a risk of an accident is disclosed. The method includes computing an anomaly score based on sensor data to obtain a series of anomaly scores. The method also includes processing the anomaly score to limit a processed anomaly score below a predetermined value. The method further includes calculating an accident risk score at time of prediction by using a series of processed anomaly scores up to the time of the prediction. The method includes further outputting a prediction result based on the accident risk score.

Abstract: Provided is an apparatus comprising a processor and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to: acquire a candidate for a solution of an optimization problem for optimizing a third objective function based on a first objective function and a second objective function; obtain, as another candidate for the solution of the optimization problem, a solution that optimizes the second objective function under a constraint corresponding to a value of the first objective function for the acquired candidate; and select the solution of the optimization problem from among the plurality of candidates for the solution of the optimization problem. Also provided as the first aspect are a method and non-transitory computer readable storage medium.

Abstract: A computer-implemented method executed by a robotic system for performing a positional search process in an assembly task is presented. The method includes applying forces to a first component to be inserted into a second component, detecting the forces applied to the first component by employing a plurality of force sensors attached to a robotic arm of the robotic system, extracting training samples corresponding to the forces applied to the first component, normalizing time-series data for each of the training samples by applying a variable transformation about a right tilt direction, creating a time-series prediction model of transformed training data, applying the variable transformation with different directions for a test sample, and calculating a matching ratio between the created time-series prediction model and the transformed test sample.

Abstract: Provided is 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: acquire a candidate for a solution of an optimization problem for optimizing a third objective function based on a first objective function and a second objective function; obtain, as another candidate for the solution of the optimization problem, a solution that optimizes the second objective function under a constraint corresponding to a value of the first objective function for the acquired candidate; and select the solution of the optimization problem from among the plurality of candidates for the solution of the optimization problem. Also provided as the first aspect are a method and non-transitory computer readable storage medium.

Abstract: A computer-implemented method for detecting an operation tendency is disclosed. The method includes preparing a general model for generating a general anomaly score. The method also includes preparing a specific model, for generating a specific anomaly score, trained with a set of a plurality of operation data related to operation by a target operator. The method further includes receiving input operation data. The method includes also calculating a detection score related to the operation tendency by using a general anomaly score and a specific anomaly score generated for the input operation data. Further the method includes outputting a result based on the detection score.

Abstract: A computer-implemented method is disclosed. The method includes preparing a base of an anomaly detection model for generating a score that indicates an estimation of a concentration decline. The anomaly detection model has parameters affecting the score. The method also includes preparing a set of training data, each of which includes a sequence of sensor data relating to activity performed by an individual. The method also includes optimizing the parameters of the anomaly detection model using the set of the training data so as to make a score for longer cumulative activity high as compared to shorter cumulative activity. The method further includes outputting the parameters of the anomaly detection model, in which the anomaly detection model having the parameters is used for detecting a concentration decline of a target individual.

Abstract: A computer-implemented method executed by a robotic system for performing a positional search process in an assembly task is presented. The method includes applying forces to a first component to be inserted into a second component, detecting the forces applied to the first component by employing a plurality of force sensors attached to a robotic arm of the robotic system, extracting training samples corresponding to the forces applied to the first component, normalizing time-series data for each of the training samples by applying a variable transformation about a right tilt direction, creating a time-series prediction model of transformed training data, applying the variable transformation with different directions for a test sample, and calculating a matching ratio between the created time-series prediction model and the transformed test sample.

Abstract: Groups of metrics to form objective functions may be generated by obtaining a judgment that represents whether a first subject is comparable to a second subject, obtaining a plurality of evaluation values for each of the first subject and the second subject, each evaluation value corresponding to a metric among a plurality of metrics, comparing, for each metric, a corresponding evaluation value of a first subject to a corresponding evaluation value of the second subject, and generating one or more groups based on a result of the comparison and the judgment, each group of the one or more groups including at least one metric of the plurality of metrics, wherein metrics in each group are determined to be comparable with respect to evaluating subjects.

Abstract: A method to generate a plurality of groups each including at least one of a plurality of elements. The method includes selecting at least one candidate element from the plurality of elements as a candidate to be included in a group, determining whether or not to generate the group including the at least one candidate element selected in the selecting step, based on an element evaluation value associated with each of the at least one candidate element selected in the selecting step, provided that a determination is made to generate the group in the determining step, generating the group including the at least one candidate element selected in the selecting step, and weighting the element evaluation value of each of the at least one element according to how many times the each element is included in already-generated groups to reflect the weighted element evaluation value in next group generation.

Abstract: An information processing apparatus includes a generation section configured to generate, from a state of a mobile vehicle in each position obtained by discretizing a traveling route along which the mobile vehicle travels, a state of the mobile vehicle in a next position for each use state of drive means contained in the mobile vehicle; and an optimization section configured to optimize the use state of the drive means in each position based on at least one of states of the mobile vehicle in each position generated by the generation section.

Abstract: An information processing apparatus includes a generation section configured to generate, from a state of a mobile vehicle in each position obtained by discretizing a traveling route along which the mobile vehicle travels, a state of the mobile vehicle in a next position for each use state of drive means contained in the mobile vehicle; and an optimization section configured to optimize the use state of the drive means in each position based on at least one of states of the mobile vehicle in each position generated by the generation section.

Abstract: Provided is an apparatus, method, and computer-readable storage medium for acquiring learning data that includes an evaluation of evaluation targets; generating a constraint condition to be satisfied by a value of an evaluation function that includes a weighting for each of a plurality of evaluation criteria of the evaluation target and an unknown term for each evaluation target corresponding to an unknown evaluation criterion that is not among the plurality of evaluation criteria, based on the learning data; determining a value of the unknown term and the weighting for each evaluation criterion in the evaluation function in a manner that satisfies the constraint condition; extracting a set of evaluation targets for which an evaluation of each evaluation criterion is opposite of an evaluation based on the unknown term, from among the plurality of evaluation targets; and outputting the extracted set of evaluation targets.

Abstract: Provided is 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: acquire a candidate for a solution of an optimization problem for optimizing a third objective function based on a first objective function and a second objective function; obtain, as another candidate for the solution of the optimization problem, a solution that optimizes the second objective function under a constraint corresponding to a value of the first objective function for the acquired candidate; and select the solution of the optimization problem from among the plurality of candidates for the solution of the optimization problem. Also provided as the first aspect are a method and non-transitory computer readable storage medium.

Abstract: Groups of metrics to form objective functions may be generated by obtaining a judgment that represents whether a first subject is comparable to a second subject, obtaining a plurality of evaluation values for each of the first subject and the second subject, each evaluation value corresponding to a metric among a plurality of metrics, comparing, for each metric, a corresponding evaluation value of a first subject to a corresponding evaluation value of the second subject, and generating one or more groups based on a result of the comparison and the judgment, each group of the one or more groups including at least one metric of the plurality of metrics, wherein metrics in each group are determined to be comparable with respect to evaluating subjects.

Abstract: An information processing apparatus, article of manufacture and method for discriminating between combined results of a plurality of elements. The apparatus includes: an assigning unit that assigns an element identification value to each of a plurality of basic elements, a generating unit that combines two or more basic elements, among the plurality of basic elements, so as to generate a combined element representing the two or more combined basic elements, and a calculating unit that merges, through an operation corresponding to an element combination method, a plurality of element identification values individually assigned to the plurality of basic elements contained in the combined element so as to calculate one element identification value for the combined element, where two elements are combined by an order-considered or ordered-ignored combination method, the calculating unit merges two corresponding element identification values through an operation where the commutative law holds or does not hold.

Abstract: An information processing apparatus includes a generation section configured to generate, from a state of a mobile vehicle in each position obtained by discretizing a traveling route along which the mobile vehicle travels, a state of the mobile vehicle in a next position for each use state of drive means contained in the mobile vehicle; and an optimization section configured to optimize the use state of the drive means in each position based on at least one of states of the mobile vehicle in each position generated by the generation section.

Abstract: A method to generate a plurality of groups each including at least one of a plurality of elements. The method includes selecting at least one candidate element from the plurality of elements as a candidate to be included in a group, determining whether or not to generate the group including the at least one candidate element selected in the selecting step, based on an element evaluation value associated with each of the at least one candidate element selected in the selecting step, provided that a determination is made to generate the group in the determining step, generating the group including the at least one candidate element selected in the selecting step, and weighting the element evaluation value of each of the at least one element according to how many times the each element is included in already-generated groups to reflect the weighted element evaluation value in next group generation.

Abstract: An information processing apparatus includes a generation section configured to generate, from a state of a mobile vehicle in each position obtained by discretizing a traveling route along which the mobile vehicle travels, a state of the mobile vehicle in a next position for each use state of drive means contained in the mobile vehicle; and an optimization section configured to optimize the use state of the drive means in each position based on at least one of states of the mobile vehicle in each position generated by the generation section.