Hitachi, Ltd.

Taiki Fuji

Kiyoto Ito

Kohsei Matsumoto

Toshio Moriya

Nobutaka Kimura

Kanako Esaki

Keisuke Fujimoto

A retail agent determines a parameter of an activity proposal model by using data stored in a past record database, and determines parameters of an activity determination model and an activity value evaluation model by further using base activity simulation data. Consequently, it is possible to appropriately determine parameters of a subsystem control method in a complex system which cannot be embodied as a simulator and shows a significant change with respect to past record data.

Learning management system and learning management method

A learning control system is provided in each of a plurality of subsystems of a system and includes a plurality of agents that performs learning to control of the subsystem using a control model and a learning management agent that manages and controls learning of each of the plurality of agents. The learning management agent is operated with a plurality of experiment systems including the plurality of control model sets of the plurality of agents and determines a plurality of updating control model sets in the plurality of experiment systems of a next generation from an evaluation result of the plurality of experiment systems of the current generation. The agent transmits control model information of a current generation to the learning management agent and receives control model information of the next generation from the learning management agent.

Learning control system and learning control method

A robot is provided. A measurement unit measures an object in a space. An own-position estimation unit calculates an own position of a mobile unit moving through the space by matching measurement data acquired by the measurement unit with map data representing information about the object in the space. A classification unit acquires class-based measurement data obtained by classifying the measurement data into at least two predetermined classes. A class-based own-position estimation unit estimates an own position of the mobile unit for each of the at least two predetermined classes by matching a class-based map having a position of the objects recorded therein for each class with the class-based measurement data. An integration unit configured to integrate the own positions estimated for the respective at least two predetermined classes. A control unit controls the mobile unit based on information from the integration unit.

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Tokyo, Japan

Taiki Fuji