Abstract: A value function and a policy of entropy-regularized reinforcement learning in a case where a state transition function and a reward function vary with time can be estimated by causing a computer to perform an input procedure in which a state transition probability and a reward function that vary with time are input and an estimation procedure in which an optimal value function and an optimal policy of entropy-regularized reinforcement learning are estimated by a backward induction algorithm based on the state transition probability and the reward function.

Abstract: An analysis apparatus for analyzing a causal relationship between an incidence of a predetermined disease and a predetermined intervention includes a memory; and a processor configured to execute: converting a plurality of first parameters indicative of attributes of users belonging to a population, at least two of the parameters having correlations with a predetermined strength, to a plurality of second parameters without the correlations with the predetermined strength with each other; computing a predetermined score for each of the users, using the plurality of second parameters and a parameter indicative of presence or absence of the intervention; and clustering the users belonging to the population using the score, to analyze the causal relationship.

Abstract: An information presentation device of the present invention presents information regarding a risk to a user. The information presentation device includes a processor and a memory storing program instructions that cause the processor to acquire an objective risk value regarding the user, acquire a subjective risk value regarding the user, and present the information regarding the risk to the user based on the subjective risk value and the objective risk value.

Abstract: An object is to make it possible to accurately estimate the number of movements at a high speed. A generation unit (130) generates an optimization problem for estimating, with respect to each of a plurality of areas, the number of observation targets that move from the area to each of the other areas at each observation time point, as the number of movements, based on observation values and movement probabilities, the observation values indicating, with respect to each area, the number of observation targets that are present in the area at each observation time point, and the movement probabilities indicating, with respect to each area, probabilities of movement from the area to the other areas at each observation time point. A first estimation unit (140) estimates the number of movements by solving the generated optimization problem using a Sinkhorn-Knopp algorithm. A second estimation unit (160) estimates the movement probabilities based on the observation values and the estimated number of movements.

Abstract: An estimation apparatus according to one embodiment is an estimation method for estimating a parameter of a model for obtaining a state transition probability used in model-based reinforcement learning and causes a computer to perform: an input procedure in which first data indicating a state transition history in a situation where an action of the model-based reinforcement learning is not performed and second data indicating, when an action prompting a transition to a predetermined state is performed, a degree of accepting the transition to the predetermined state are input; and an estimation procedure in which a parameter of the model is estimated by using the first data and the second data.

Abstract: An observation streamlining apparatus includes one or more computers each including a mempry and a processor configured to discriminate between an observation-necessary time slot and an observation-unnecessary time slot with an intervention measure including at least a time when a predetermined intervention is performed on a user as an input, the observation-necessary time slot indicating a time slot when a user’s action or state needs to be observed, and the observation-unnecessary time slot indicating a time slot when the user’s action or state does not need to be observed; and execute predetermined processing for observing the user’s action or state when the observation-necessary time slot arrives.

Abstract: A training apparatus includes a feature extraction unit that extracts feature vector data from behavior data of each date and time, a reference point extraction unit that performs, for each date and time, processing of calculating a difference between feature vector data of certain date and time and each of one or more pieces of feature vector data in past within a predetermined period from the date and time, and extracting one or more pieces of difference vector data corresponding to the feature vector data of the date and time, and a state estimation model training unit that trains a state estimation model using feature vector data of each date and time, difference vector data, and state information.

Abstract: The present disclosure enables vehicle dispatch in consideration of individual differences of each orderer for a price and a required time by a computer executing an input procedure to input parameters for a distance matrix relating to a distance between a taxi and an orderer giving a taxi dispatch order, a travel distance for an order, an opportunity cost parameter for a taxi driver, and an acceptance probability function of the orderer, and a calculation procedure to calculate a price and a required time to be presented to the orderer by solving an optimization problem formulated using the parameters.

Abstract: Markov chain parameters can be accurately estimated using transition data whose observation interval is not constant. Assuming that transition intervals of a Markov chain defined from a set of states are steps, input data that is transition data including the number of transitions between states in a set of transitions between states is received, and a parameter relating to a model regarding the number of steps representing a probability that a transition of a predetermined number of steps occurs from each state and a parameter relating to a model regarding a transition probability representing a probability that a one-step transition occurs from each state, the models being models for the number of transitions between the states of the input data, are estimated such that an objective function including a term for a generation probability of the input data given by the model regarding the number of steps and the model regarding the transition probability is optimized.

Abstract: Markov chain parameters can be accurately estimated using partially observed data. A set of states, a set of observable states, censored transition data regarding the set of observable states, and perfect transition data that is complete data on transitions between states in the set of states are received as input data. Parameters relating to transition probabilities of a predetermined Markov chain defined from the set of states and a censored Markov chain defined from the set of observable states are estimated such that an objective function including a term representing a degree of agreement of the transition probability of the predetermined Markov chain, the degree of agreement thereof indicating a degree of fit to the perfect transition data, and a term representing a degree of agreement of the transition probability of the censored Markov chain, the degree of agreement thereof indicating a degree of fit to the censored transition data, is optimized.

Abstract: An action-sequence generation unit of an information presentation device generates, based on an action log representing a target action of a user and an action history of the user, an action sequence representing a sequence of actions including the target action. A permission-level calculation unit calculates, based on the action log and a permission log, about the actions, a permission level representing a difference between a time period when the actions included in the action log were performed and a time period when the actions included in the permission log are permitted. An action specifying unit specifies, based on the action sequence and the permission level, out of the actions in the action sequence, an action, the permission level of which is equal to or larger than a threshold concerning the permission level, as a trigger action among the actions included in the action sequence.

Abstract: A state acquisition unit of an information presentation device acquires a state of a user. Then, an action information acquisition unit acquires an action according to the state acquired by the state acquisition unit by inputting the state acquired by the state acquisition unit to a learning model or a learned model for outputting the action according to the state from the state of the user, the learning model or the learned model being subjected to reinforcement learning based on a reward function which outputs a reward according to the state of the user relative to a target state of the user. Then, an information output unit outputs the action acquired by the action information acquisition unit.

Abstract: A learning device includes a learning unit that learns parameters for determining an occurrence probability of an event at each time and each location on the basis of history information relating to the event, the history information including a time, a location, and an event type, and features of an area corresponding to the location, so that a likelihood expressing a combined effect of the event type and the features of the area on the event is optimized.

Abstract: An estimation apparatus includes an estimation unit configured to estimate an acceptability tensor corresponding to an activity tensor representing an activity log without a corresponding acceptability log, by using a result of previously performed learning of a relationship between an activity tensor representing an activity log recording activities of a user, and an acceptability tensor representing an acceptability log recording an acceptability for a time change of the activities of the user.

Abstract: An optimization device includes a model construction unit that constructs a model for representing a relationship among groups and for obtaining a prediction represented as a time series based on a set of groups of occurrence time points of reference events as events occurring before interventions and intervention timings as time points to cause the interventions and a set of evaluation values of the groups, a parameter determination unit that acquires one or more occurrence time points of the reference events and determines the next group including a next intervention timing based on the acquired occurrence time points of the reference events, the constructed model, and an acquisition function for obtaining the next intervention timing, an evaluation unit that performs the intervention at the next intervention timing in the determined next group and calculates the evaluation value of the group obtained as the next group, and an assessment unit that causes construction of the model, determination of the group

Abstract: A device estimates a number of people moving between the areas of people by building a problem based on a population in each of areas at each of time points and a probability of movement between predetermined areas in a directed graph. The directed graph includes vertices that correspond to the areas and edges that correspond to movement paths between the areas. A cost function for each edge determined from the probability of movement satisfies a constraint of discrete convexity representing a monotonous increase in change of a function value. The device estimates the number of people moving by computing the problem using a predetermined algorithm and estimating a probability of movement between the areas at each of the time points by minimizing a cost for the problem. The device repeats the estimating until satisfying a predetermined condition.

Abstract: In a learning apparatus, in a neural network into which low-resolution data having a low resolution and representing demographics including positions and densities, first auxiliary information related to types of locations in an area and positions of the locations, and second auxiliary information representing at least one of a time of day, weather, or another information representing a change in time series are input, and from which resolution enhanced data with resolution of the demographics being enhanced is output, resolution enhanced intermediate data with a resolution of the low-resolution data for learning for each set of an area and a time zone being enhanced is determined, weights for the types of locations using the first auxiliary information for the types of locations and the second auxiliary information, for each set of an area and a time zone is determined, resolution enhanced data that is the first auxiliary information weighted by the weight for each of the types of locations being integrated

Abstract: The parameter estimation section 106 estimates a parameter for transition probability, a parameter for inflow noise, a parameter for outflow noise, and a number of moving people based on inflow data and outflow data so as to optimize an objective function. The inflow data is aggregate data made up of an inflow number of people at each time step for each of plural observation points and the outflow data is aggregate data made up of an outflow number of people at each time step for each of the plural observation points.

Abstract: A parameter estimation section 106 is configured to perform estimation, for aggregate data in which values are associated with respective regions obtained by subdividing a space and for a Gaussian process model that expresses a plurality of aggregate data of differing partition granularity. The estimation is performed based on the Gaussian process model including a spatial scale parameter of a correlation function between regions of the aggregate data and including a noise variance parameter of the correlation function, by estimating the spatial scale parameter and the noise variance parameter so as to maximize a function expressing values of the aggregate data by area integrals of a Gaussian process.

Abstract: To estimate a parameter of a Markov chain model including unobservable states. An input unit (101) receives input data including a state set of a Markov chain to be estimated, a set of observable states, and censored transition data represented by a transition between the observable states and initial states of the observable states, an estimation unit (102) optimizes an objective function including a term representing a degree of match of a transition probability of a first Markov chain generating the censored transition data and a transition probability of a second Markov chain made from a model representing the Markov chain to be estimated and the set of the observable states, by using a parameter, and estimates the parameter, and an output unit (103) outputs the parameter estimated.