Abstract: An information processing apparatus (20) includes a model generating unit (210) and a feature value computation unit (220). The model generating unit (210) generates an Auto-Regressive with eXogenous input (ARX) model of a smell sensor by use of input data controlling an input operation of gas including a smell component being a measurement target, and output data acquired by inputting the gas to the smell sensor, based on the input data. The feature value computation unit (220) computes a transfer function of the smell sensor relating to the smell component by subjecting the ARX model to Z-Transform, and further computes a first-order lag transfer function feature value of the smell sensor relating to the smell component by subjecting the transfer function to partial fraction decomposition.

Abstract: The function input means 71 accepts input of a cost function that calculates a cost incurred in at least one of a selection of a candidate relay point and a design of a route, the cost function being represented as a linear sum of terms weighted by degree of importance attached to each of the features that an expert is assumed to intend when selecting the candidate relay point and design of the route. The learning means 72 learns the cost function by inverse reinforcement learning using training data that includes relay point information which is data that maps information indicating a relay point with surrounding information of the relay point and usage information of the relay point, and route result information which is result data of a route that pass through each relay point.

Abstract: A function input means 91 accepts input of a reward function whose features are set to satisfy a Lipschitz continuity condition. An estimation means 92 estimates a trajectory that minimizes Wasserstein distance, which represents distance between probability distribution of a trajectory of an expert and probability distribution of a trajectory determined based on parameters of the reward function. An update means 93 updates the parameters of the reward function to maximize the Wasserstein distance based on the estimated trajectory.

Abstract: In a noise removing apparatus, a data acquisition unit acquires sets of odor data measured using a sensor with respect to a plurality of objects, each set of odor data representing features of an odor of an object by respective rates of a plurality of odor molecules. A noise component extraction unit extract a noise component using a set of odor data. A noise removing unit removes the noise component from each set of odor data to be processed.

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: An information processing apparatus (20) includes a sensor output data acquisition unit (210), a prediction equation generation unit (220), and an operation setting unit (230). The sensor output data acquisition unit (210) acquires sensor output data for each sampling length of an odor sensor with respect to a target gas. The prediction equation generation unit (220) generates, by using the sensor output data for each sampling length, a prediction equation for making a prediction for an odor component of the target gas. The operation setting unit (230) determines, by using the prediction equation, a sampling length for operating the odor sensor.

Abstract: An input means 81 accepts input of an extended objective function, in which each term indicative of a score of each classification result in an objective function of classification analysis is multiplied by a bias parameter as a parameter indicative of a degree of bias of the score of each classification result concerned. An optimization means 82 optimizes a logistic regression weight in the extended objective function. An estimation means 83 estimates the bias parameter by inverse reinforcement learning using the extended objective function of logistic regression to which the optimized weight is set.

Abstract: A first inverse reinforcement learning execution unit 91 derives each weight of candidate features, which are plural features as candidates, included in a first objective function by inverse reinforcement learning using the candidate features. A feature selection unit 92 selects a feature when one feature is selected from the candidate features, from which each weight is derived, in such a manner that a reward represented using the feature is estimated to get the closest to an ideal reward result. A second inverse reinforcement learning execution unit 93 generates a second objective function by inverse reinforcement learning using the selected feature.

Abstract: An information processing device is configured to include an acquisition unit, a determination unit, an instruction unit, an instruction unit, and output unit. The acquisition unit is configured to acquire measurement target and measurement environment information, and measurement environment information that a measurer can measure with an odor sensor. The determination unit is configured to determine a measurement target that the measurer should be instructed to measure, based on the measurement target and measurement environment information and the measurement environment information that can be measured, the instruction is configured to instruct the measurer to measure the determined measurement target. The output unit configured to output a reward to the measurer after the acquisition means acquires odor data of the determined measurement target.

Abstract: The first output means 81 outputs a second target, which is an optimization result for a first target using an objective function generated in advance by inverse reinforcement learning based on decision making history data indicating an actual change to the target. The second output means 82 outputs a third target indicating a target resulting from further changing of the second target based on a change instruction regarding the second target accepted from the user. The data output means 83 outputs the actual change from the second target to the third target as decision making history data. The learning means 84 learns the objective function using the decision making history data.

Abstract: The target output means 91 outputs a plurality of second targets, which are optimization results for a first target using one or more objective functions generated in advance by inverse reinforcement learning based on decision making history data indicating an actual change to a target. The selection acceptance means 92 accepts a selection instruction from a user for a plurality of the output second targets. The data output means 93 outputs the actual change from the first target to the accepted second target as the decision making history data. The learning means 94 learns the objective function using the decision making history data.

Abstract: The output means 81 outputs a diagram to a display device. The input means 82 accepts designation of a change point and a change condition for the displayed diagram. The constraint generation means 83 generates a constraint for an objective function used for optimization of the diagram based on the designation. The change proposal generation means 84 generates a change proposal for the diagram by optimizing the objective function based on the generated constraint. Then, the input means 82 accepts, for each change point, the designation of a hard constraint indicating a condition that must be satisfied, or a soft constraint indicating a condition that increases a penalty according to degree of unsatisfactory, as the designation of the change condition, the constraint generation means 83 generates the constraint according to the hard constraint or soft constraint, and the output means 81 outputs the change proposal of the diagram.

Abstract: The congestion degree calculation means 81 calculates a congestion degree at a vehicle and a stop. The diagram output means 82 outputs a modified diagram in which a current diagram is modified by optimizing an objective function learned using business history data including an actual change of a diagram. The risk calculation means 83 calculates a current risk which is a risk occurring at the present time, and a modification risk which is a risk caused by modifying the diagram, based on the congestion degree. The risk output means 84 outputs the calculated current risk and the modification risk.

Abstract: In a data generation apparatus, an acquisition unit acquires original data which are odor data measured in a specific environment. A generation unit performs a linear transformation with respect to the original data, and generates augmented data which are odor data in an environment where temperature and humidity are different from those in the specific environment.

Abstract: In a noise removing apparatus, a data acquisition unit acquires sets of odor data measured using a sensor with respect to a plurality of objects, each set of odor data representing features of an odor of an object by respective rates of a plurality of odor molecules. A noise component extraction unit extract a noise component using a set of odor data. A noise removing unit removes the noise component from each set of odor data to be processed.

Abstract: A server apparatus 10 is communicably connected to a terminal apparatus 20 that collects sensor data from an odor sensor 40. The server apparatus 10 includes an analyzer holding unit 11 that holds a plurality of analyzers for analyzing specific odor analysis targets, based on sensor data, an analyzer management unit 12 that determines preprocessing to be performed on the sensor data, by selecting an analyzer according to the environment of the odor sensor 40, and causes the terminal apparatus 20 to execute the preprocessing, an analysis execution unit 13 that executes analysis processing of the designated odor analysis target, by applying the selected analyzer to the preprocessed sensor data, and an analysis result transmission unit 14 that transmits information indicating a result of the analysis processing to the terminal apparatus 20.

Abstract: A training data generation device includes a label candidate generation unit, a reception unit, and a training data generation uni. The acquisition unit is configured to acquire smell data and information pertaining to the smell data. The label candidate generation unit which generates label candidates on the basis of the information pertaining to the smell data; an output unit which outputs the generated label candidates. The reception unit is configured to receive selection of a label from the output label candidates. The training data generation unit which generates training data from the selected label and the smell data.

Abstract: An input unit 81 receives input of a decision-making history of a subject. A learning unit 82 learns hierarchical mixtures of experts by inverse reinforcement learning based on the decision-making history. An output unit 83 outputs the learned hierarchical mixtures of experts. The learning unit 82 learns the hierarchical mixtures of experts using an EM algorithm, and when a learning result using the EM algorithm satisfies a predetermined condition, learns the hierarchical mixtures of experts by factorized asymptotic Bayesian inference.

Abstract: A learning unit 80 includes an input unit 81, a reward function estimation unit 82, and a temporal logic structure estimation unit 83. The input unit 81 receives input of an action history of a worker who performs multiple tasks in time series. The reward function estimation unit 82 estimates a reward function for each task in time series based on the action history. The temporal logic structure estimation unit 83 estimates a temporal logic structure between tasks based on a transition condition candidate at a point in time when each estimated reward function switched.

Abstract: An information processing apparatus (2000) acquires a feature vector (20) obtained based on signal data (14) of a detected value of a sensor (10) that senses gas to be measured. The information processing apparatus (2000) decomposes the feature vector (20) into a product of a coefficient vector and a feature matrix by using a non-negative matrix factorization (NMF). The detected value of the sensor (10) changes according to an attachment and a detachment of a molecule contained in a sensed gas. A value of each element of the feature vector (20) is equal to or greater than zero.