Abstract: A learning device includes at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: perform reinforcement learning of control over a control target; use data used in the reinforcement learning to learn a model that shows the relationship between a state relating to the control target, control over the control target, and a temporal change in the state relating to the control target; and use the model and the result of the reinforcement learning to learn control over the control target.

Abstract: A control device calculates, by simulation of a control target, information indicating a trend of response of the control target. The control device calculates, based on the information indicating the trend of the response of the control target, an input value to the control target in order to bring an output value of the control target closer to an envisaged value.

Abstract: An estimation device estimates the relationships among a plurality of items, on the basis of at least one of: the state of a prediction model that receives input of past values of the items or past values of some of the items and then outputs a prediction value for at least one of the items; and differences in the prediction accuracy of the prediction model with respect to different inputs.

Abstract: A setting device sets a parameter value of a simulator that simulates a simulation subject. The setting device calculates weights of respective output items based on an output value of the simulator and a reference output value. The reference output value is subject to simulation according to the output value of the simulator. The setting device controls, by using an evaluation function, learning of a setting of a parameter value of the simulator. The evaluation function is based on the output value of the simulator, the reference output value, and the weights.

Abstract: A simulator device uses, among passenger action ratios linked to selection conditions, a passenger action ratio that is linked to a selection condition matching the type of moving body stopping in a simulation for a traffic system to calculate the number of people alighting from the moving body and/or the number of people boarding the moving body.

Abstract: An estimation device determines a state estimation target portion of a monitoring target based on qualitative inference that uses designated portion qualitative expression information qualitatively indicating a state of a designated portion of the monitoring target, and acquires state candidate qualitative expression information that qualitatively indicates a candidate for the state of the state estimation target portion. The estimation device acquires state candidate quantitative expression information that quantitatively indicates a candidate for the state of the state estimation target portion, based on the state candidate qualitative expression information.

Abstract: A training device updates, among a plurality of models, a model for a region that includes a given sample based on the sample. The plurality of models are provided for each region obtained by dividing a state space that includes a sample indicates a state about a control object and the plurality of models represent an evaluation of an action of the control object in response to control over the control object. The training device evaluates an action of the control object in a given state, based on a model for a region that includes a sample indicating the state.

Abstract: An information processing device acquires training data. The training data includes time-series data of a measurement item regarding a target and time-series data of an item that influences the target. The information processing device trains, by using the training data, a model that receives an input of time-series data of the measurement item and outputs time-series data of the item that influences the target.

Abstract: An operation determination device adapted to a system including a carriage machinery for carrying carriage targets and facilities relating to an operation of the carriage machinery includes a determination means configured to produce an operation plan of the carriage machinery to improve a quantitative evaluation status of the carriage targets using a simulator configured to simulate the operation of the carriage machinery.

Abstract: An inference device performs a qualitative inference using an inference rule which, from an input of a value indicating an increase and a value indicating a decrease, derives an output representing any one of an increase, a decrease, or no change.

Abstract: An information processing device includes: a plurality of linear combination nodes that linearly combine input values; a selection node that is provided to the linear combination node and calculates, according to the input values, a value indicating whether or not a corresponding linear combination node is selected; and an output node that outputs an output value calculated based on a value of the linear combination node and a value of the selection node.

Abstract: An inference device makes an inference in accordance with first-order predicate logic by using: a true or false of a proposition that determines a state of a target by using a predicate that represents the state of the target; and an inference rule where a predicate representing a state of a target in an antecedent differs from a predicate representing a state of a target in a consequent. The predicate representing the state of the target in the antecedent and the predicate representing the state of the target in the consequent represent a proposition that determines a state of a target in a different time step.

Abstract: Target state inference unit infers a target state of a system and a partial target state thereof between a first state of the system and the target state thereof based on the first state, inference knowledge, and quantitative knowledge, the system being configured to be operated based on a manipulation procedure. Manipulation sequence inference unit infers a manipulation for a transition to the partial target state based on a manipulation derivation rule. Learning setting generation unit generates a learning setting for the inferred manipulation based on a learning setting derivation rule. A learning agent creates information about detailed manipulations in the manipulation based on the learning setting for the manipulation.

Abstract: A text processing system that is able to appropriately determine textual entailment between sentences with high coverage is provided. The text processing system is configured to execute: processing of extracting a common substructure that is a partial structure of a same type, the partial structure being common to a first sentence and a second sentence and, based on the a structure representing the first sentence and a structure representing the second sentence; processing of extracting at least one of a feature amount representing a dependency relationship between the at least one common substructure in the first and second sentences and a feature amount representing a dependency relationship between the common substructure in the first and second sentences and a substructure different from the common substructure; and processing of determining an entailment relationship between the first sentence and the second sentence by using the extracted feature amount.

Abstract: A text processing system that is able to appropriately determine textual entailment between sentences with high coverage is provided. The text processing system is configured to execute: processing of extracting a common substructure that is a partial structure of a same type, the partial structure being common to a first sentence and a second sentence and, based on the a structure representing the first sentence and a structure representing the second sentence; processing of extracting at least one of a feature amount representing a dependency relationship between the at least one common substructure in the first and second sentences and a feature amount representing a dependency relationship between the common substructure in the first and second sentences and a substructure different from the common substructure; and processing of determining an entailment relationship between the first sentence and the second sentence by using the extracted feature amount.