Abstract: An information processing apparatus includes an acquisition unit for obtaining target data, a designation unit for designating one or a plurality of explanatory variables and one or a plurality of target variables from a plurality of features included in the target data, and a derivation unit for deriving a first linear combination of the one or plurality of explanatory variables, the first linear combination defining an upper limit of the target variable, and a second linear combination of the one or plurality of explanatory variables, the second linear combination defining a lower limit of the target variable.

Abstract: This visualization device comprises a coefficient acquisition unit and an output control unit. The coefficient acquisition unit acquires, for each of a plurality of different objective functions, a weight coefficient for each of a plurality of feature amounts. The output control unit outputs, in a comparable manner, the acquired weight coefficients for the plurality of objective functions. The weight coefficients for the plurality of feature amounts express the degree of influence of the feature amounts on solutions to the plurality of objective functions.

Abstract: Provided is an information processing apparatus which is capable of assigning highly accurate interior division proportion parameters. This information processing apparatus includes an acquiring section for acquiring target data, the number of a plurality of target models, and information regarding a prior distribution of a latent variable, a regression coefficient computing section for computing respective regression coefficients of the plurality of target models, a covariance computing section for computing a covariance parameter of the prior distribution of the latent variable and a covariance matrix of a posterior distribution of the latent variable, an interior division proportion parameter computing section for computing the interior division proportion parameters, and an output section for outputting the regression coefficients and the interior division proportion parameters.

Abstract: A reward function estimation unit 81 estimates a reward function by multiple importance sampling using samples of a decision-making history of a subject and of a decision-making history generated based on a sampling policy. A policy estimation unit 82 estimates a policy by reinforcement learning using the estimated reward function. The reward function estimation unit 81 sets the policy estimated by the policy estimation unit as a new sampling policy, and estimates the reward function by the multiple importance sampling using the samples of the decision-making history of the subject and of the decision-making history generated based on the sampling policy.

Abstract: An information processing apparatus includes an acquisition unit that acquires feature values related to a target product and similar product information related to a product similar to the target product, a first prediction unit that calculates, by using a plurality of prediction models, a prediction value for each prediction model from the feature values, a selection unit that selects a metamodel related to the similar product information, and a second prediction unit that performs, by using the selected metamodel, prediction related to the target product from the prediction values calculated by the prediction models.

Abstract: A demand prediction device is a device predicting a demand quantity for a target product based on a prediction model, and the demand prediction device specifies a similar product to the target product, calculates a base demand quantity for the target product by using the prediction model weighted in accordance with the similar product and a feature of the target product, the prediction model is a model in which two or more types of individual learning models are integrated and a weigh is set for each of the individual learning models, the individual learning model is a model that outputs the demand quantity in accordance with a feature of a product, receives an operation of changing the weight for each of the individual learning models, and calculates a predicted demand quantity for the target product by using the prediction model. The present disclosure can be used to support decision making.

Abstract: An input unit 81 receives inputs of pre-change performance data acquired by a device before a change and post-change performance data acquired by the device after having undergone the change, through control using a first cost function. An update unit 82 generates a second cost function obtained by updating the first cost function in such a way as to reduce a difference between the pre-change performance data and the post-change performance data. In the process, the update unit 82 generates the second cost function obtained by updating the first cost function by estimating an error that occurs in an output value of the device included in the first cost function before and after the change to the device.

Abstract: A new product demand prediction apparatus according to the present disclosure includes: a memory configured to store instructions; and one or more processors configured to execute the instructions to: acquire new product information including at least information regarding promotion of a new product that is a target of demand prediction; acquire first similar product information indicating a sales performance of a first similar product selected based on at least one element of a scale of the promotion, a characteristic of a product, and a sales method; acquire second similar product information indicating a sales performance of a second similar product selected based on the category of the product; and predict sales in a predetermined period including a period after a promotion period of the new product based on the new product information, the first similar product information, and the second similar product information.

Abstract: This visualization device comprises an acquisition unit and an output control unit. The acquisition unit acquires, for each of a plurality of different solutions to an optimization problem which are obtained on the basis of an objective function, feature amount values of the objective function for each solution. The output control unit outputs, in a comparable manner, the feature amount values acquired for the optimal solution among the plurality of solutions and the feature amount values acquired for executable solutions among the plurality of solutions.

Abstract: In order to generate a workout schedule in consideration of a state regarding a workout, a workout support apparatus (2) includes: a data acquiring section (21) for acquiring state data which indicates a state regarding a workout done by a targeted person; and a generating section (22) for generating a workout schedule in accordance with the state indicated by the state data, by performing an optimization calculation with use of an objective function, the objective function being generated by inverse reinforcement learning with use of training data which indicates a workout schedule that is in accordance with a state regarding a workout and that is to be applied in the state.

Abstract: An extraction system includes an output unit, an acquisition unit, and an update unit. The output unit outputs a feature amount used for similarity determination by an extraction model that extracts a product similar to a target product, a set value of an importance level of the feature amount, and a change field for changing the set value of the importance level of the feature amount. The acquisition unit acquires a change value for changing the set value of the importance level of the feature amount input in the change field of the output screen. The update unit updates the set value of the importance level of the feature amount in the extraction model based on the change value. The extraction system can be used, for example, to support decision making.

Abstract: An AI (Artificial Intelligence) system comprising: the input means 81 accepts input of multiple candidate solutions to an optimization problem in which an objective function is express as an inner product of a feature and weight, or expressed in a bilinear form, and weight of the objective function; the optimal solution determination means 82 determines as an optimal solution, among the candidate solutions, the candidate solution that maximizes the inner product with the weight of the objective function or the candidate solution that maximizes a value of the bilinear form; and the output means 83 outputs the optimal solution.

Abstract: A demand prediction device acquires feature information indicating a feature related to a target product, calculates a base demand quantity for the target product for each prediction model by using two or more types of prediction models outputting a demand quantity for a product and the feature information of the target product, receives selection of at least one of the two or more types of prediction models and an operation of a parameter affecting output of the demand quantity for the selected prediction model, and calculates a predicted demand quantity for the target product by using a prediction model reflecting the operation. The present disclosure can be used to support decision making.

Abstract: In order to generate a more efficient operation plan as an operation plan regarding a water distribution plan, an information processing apparatus (1) includes: an acquisition means (11) for acquiring target data regarding a target water distribution plan; and a generation means (12) for generating an operation plan regarding the target water distribution plan by solving an optimization problem that uses (i) a cost function determined by inverse reinforcement learning which uses reference data regarding a reference water distribution plan and (ii) the target data acquired by the acquisition means.

Abstract: An optimization device according to the present disclosure comprises: an optimization display means for displaying an optimization result that has been obtained on the basis of an objective function used in the optimization of an event; a change reception means for receiving a change in a parameter that determines a calculated value regarding feature constituting the objective function; an optimization execution means for optimizing the event on the basis of the changed parameter; and an optimization updating means for updating and displaying the optimization result.

Abstract: The input means 81 accepts input of observation data observed along with an operation of equipment and input of a cost function whose explanatory variable is a factor of action intended by equipment operator. The learning means 82 generates the cost function by inverse reinforcement learning using the observation data. The distribution map generation means 83 extracts weight of the explanatory variable of the generated cost function as a feature representing an intention of the operator, and generates a distribution map in which information on the cost function is placed at corresponding positions in a multidimensional space with the explanatory variables as dimensional axes according to the extracted feature.

Abstract: The search means 91 searches for an optimization problem matching a specified search condition from a database that stores search information that associates first data indicating an optimization problem including an objective function and a constraint with second data indicating a feature of the optimization problem. The input means 92 accepts input of the second data as search condition.

Abstract: The input means 81 accepts input of first data indicating an optimization problem including an objective function and constraints, and second data indicating a feature of the optimization problem. The generation means 82 generates search information that associates the first data with the second data.

Abstract: An information processing apparatus (20) includes a use environment information acquisition unit (210), a model selection unit (220), and a prediction unit (230). The use environment information acquisition unit (210) acquires use environment information indicating a use environment of a physical system having input-output. The model selection unit (220) selects, from a storage unit storing a plurality of prediction models of the physical system in association with section information indicating a section based on the use environment, a prediction model being associated with section information of a section matching the use environment indicated by the use environment information. The prediction unit (230) performs prediction based on output of the physical system by use of the selected prediction model.

Abstract: A learning model generation support apparatus 10 is an apparatus for supporting generation of a learning model to be utilized in odor detection using an odor sensor that reacts to a plurality of types of odors. The learning model generation support apparatus 10 includes a data acquisition unit 11 that acquires sensor data output by the odor sensor under specific measurement conditions and condition data specifying the measurement conditions, and inputs, as training data, the acquired sensor data and condition data to a machine learning engine 31 that generates the learning model, and a condition setting unit 12 that acquires a predictive accuracy output by the machine learning engine in response to input of the training data, and sets new measurement conditions for when the odor sensor newly outputs sensor data as training data, based on the acquired predictive accuracy.