Abstract: An information processing apparatus includes: readout processor circuitry configured to read out a tree-structured learned model that is obtained by performing a learning process on a tree-structured model by using a first data set; leaf node identification processor circuitry configured to input a second data set to the tree-structured learned model and identify a first leaf node that is a leaf node corresponding to the second data set in the tree-structured learned model; and a ratio information generator configured to generate information related to a ratio between a number of all leaf nodes of the tree-structured learned model, and a number of the first leaf nodes or a number of second leaf nodes. The second leaf nodes are leaf nodes that do not each correspond to the first leaf node among the leaf nodes of the tree-structured learned model.

Abstract: An information processing apparatus includes search condition specification processor circuitry that specifies a search condition on the basis of a branch condition associated with each node of one or a plurality of tree structure models and related to a manipulable variable, and input data specification processor circuitry that specifies input data from which target output data or output data close to the target output data is to be generated in the tree structure model on the basis of the search condition.

Abstract: A control device includes a first controller configured to generate a first operation amount for the device on the basis of an output fed back from the device and a target value, a predicted output generator including a learned model which is machine learned so as to generate a predicted output from the device on the basis of the output fed back from the device and the first operation amount, a second controller configured to generate a second operation amount for the device on the basis of the predicted output and the target value, an integrated operation amount generator configured to generate an integrated operation amount which is an operation amount for the device on the basis of the first operation amount and the second operation amount.

Abstract: An information processing apparatus includes data acquiring processor circuitry configured to acquire input data and correct answer data that corresponds to the input data, an inferred output data generator configured to generate inferred output data of an ensemble learning-type inference model by inputting the input data to the ensemble learning-type inference model that performs inference based on each inference result by a plurality of inference models, and an additional learning processor configured to perform additional learning processing with respect to a part of or all of each of the inference models that constitute the ensemble learning-type inference model by using an update amount based on the inferred output data and the correct answer data.

Abstract: An information processing device for generating tree structure model data to be used for machine learning based on data for generating a tree structure model including one or more pieces of input sequence data, includes statistic identifying processor circuitry that identifies a statistic for each piece of the input sequence data, and a tree-structure-model-data generator that sets a splitting value for each node of a tree structure model based on the statistic, thereby generating tree structure model data.

Abstract: An information processing device generates a prediction output corresponding to input data. The information processing device includes input-node specification processor circuitry, based on the input data, configured to specify input nodes corresponding to the input data and each located on a corresponding one of layers from beginning to end of the learning tree structured, reliability-index acquisition processor circuitry configured to acquire a reliability index obtained through the predetermined learning processing and indicating prediction accuracy, output-node specification processor circuitry, based on the reliability index acquired by the reliability-index acquisition processor circuitry configured to specify, from the input nodes corresponding to the input data, an output node that is the basis of the generation of a prediction output, and prediction-output generation processor circuitry configured to generate a prediction output.

Abstract: Generating a universal learned model that appropriately controls a group of operating devices having the same configuration. Steps comprise subjecting a predetermined machine learning model to learning based on predetermined initial data to generate an initial learned model and an integration step of incorporating the initial learned model that controls a predetermined operating device into a plurality of operating devices, and integrating a plurality of individual learned models obtained by additional learning based on respective operation data obtained by operating the respective operating devices, thereby providing a universal learned model.

Abstract: An information processing device includes reference input data acquisition processor circuitry, a first output data generator configured to generate first output data by inputting the reference input data to a first approximation function generated based on training input data and training correct data, a second output data generator configured to generate second output data by inputting the reference input data to a second learned model generated by performing machine learning based on the training input data and difference data between output data generated by inputting the training input data to the first approximation function, and the training correct data, a final output data generator configured to generate final output data based on the first output data and the second output data; reference correct data acquisition processor circuitry, and update processor circuitry configured to update the second learned model by performing machine learning based on difference data.

Abstract: An information processing apparatus includes a candidate generator configured to generate a plurality of data division criterion candidates by generating data division criterion candidates on the basis of a plurality of data pieces arbitrarily selected from the data pieces to be divided at nodes that constitute the tree structure and hold the data pieces to be divided, data division processor circuitry configured to divide the data pieces to be divided on the basis of the plurality of data division criterion candidates to generate a plurality of data division results, evaluation processor circuitry configured to evaluate the data division results to respectively generate evaluation results, and division criterion determination processor circuitry configured to determine one data division criterion candidate among the plurality of data division criterion candidates as a data division criterion on the basis of the evaluation results.

Abstract: A learned model integration method for integrating multiple different learned models obtained by letting a learning model learn a predetermined data group, the learning model having a tree structure in which multiple nodes associated with respective hierarchically divided state spaces are hierarchically arranged, the method includes: a data reading step of reading data related to the multiple different learned models from a predetermined memory unit; and an integrating step in which, for each node constituting a tree structure related to the multiple different learned models, when a node exists in only one learned model, the node is duplicated, and when nodes exist in corresponding positions in the multiple learned models, the corresponding nodes are integrated, thereby integrating the multiple different learned models into a single learned model.

Abstract: A control device includes a first controller configured to generate a first operation amount for the device on the basis of an output fed back from the device and a target value, a predicted output generator including a learned model which is machine learned so as to generate a predicted output from the device on the basis of the output fed back from the device and the first operation amount, a second controller configured to generate a second operation amount for the device on the basis of the predicted output and the target value, an integrated operation amount generator configured to generate an integrated operation amount which is an operation amount for the device on the basis of the first operation amount and the second operation amount.

Abstract: An information processing device performs machine learning utilizing a tree structure model configured by branching and hierarchically arranging a plurality of nodes respectively corresponding to hierarchically divided state spaces, the information processing device including: a learning object dataset reader configured to read a learning object dataset formed of a plurality of input columns and one or more output columns; an importance degree calculator configured to calculate importance degrees of the individual input columns based on the learning object dataset; an order generator configured to generate an order of the individual input columns to be a base of branch determination of the individual nodes, based on the individual importance degrees; and a machine learning circuitry configured to perform the machine learning based on the learning object dataset and the order.

Abstract: A data amount compressing method for compressing a data amount corresponding to a learned model obtained by letting the learning model learn a predetermined data group, the learning model having a tree structure in which multiple nodes associated with respective hierarchically divided state spaces are hierarchically arranged, wherein each node in the learned model is associated with an error amount that is generated in the process of the learning and corresponds to prediction accuracy, and the data amount compressing method includes: a reading step of reading the error amount associated with each node; and a node deleting step of deleting a part of the nodes of the learned model according to the error amount read in the reading step, thereby compressing the data amount corresponding to the learned model.

Abstract: There is provided an information processing device that generates a prediction output corresponding to input data, the information processing device, including: an input-node specification unit that, based on the input data, specifies input nodes corresponding to the input data and each located on a corresponding one of layers from beginning to end of the learning tree structure; a reliability-index acquisition unit that acquires a reliability index obtained through the predetermined learning processing and indicating prediction accuracy; an output-node specification unit that, based on the reliability index acquired by the reliability-index acquisition unit, specifies, from the input nodes corresponding to the input data, an output node that is the basis of the generation of a prediction output; and a prediction-output generation unit that generates a prediction output, based on the to-be-learned data included in the state space corresponding to the output node specified by the output-node specification unit

Abstract: A learned model integration method for integrating multiple different learned models obtained by letting a learning model learn a predetermined data group, the learning model having a tree structure in which multiple nodes associated with respective hierarchically divided state spaces are hierarchically arranged, the method includes: a data reading step of reading data related to the multiple different learned models from a predetermined memory unit; and an integrating step in which, for each node constituting a tree structure related to the multiple different learned models, when a node exists in only one learned model, the node is duplicated, and when nodes exist in corresponding positions in the multiple learned models, the corresponding nodes are integrated, thereby integrating the multiple different learned models into a single learned model.

Abstract: A data amount compressing method for compressing a data amount corresponding to a learned model obtained by letting the learning model learn a predetermined data group, the learning model having a tree structure in which multiple nodes associated with respective hierarchically divided state spaces are hierarchically arranged, wherein each node in the learned model is associated with an error amount that is generated in the process of the learning and corresponds to prediction accuracy, and the data amount compressing method includes: a reading step of reading the error amount associated with each node; and a node deleting step of deleting a part of the nodes of the learned model according to the error amount read in the reading step, thereby compressing the data amount corresponding to the learned model.