An autonomous driving apparatus for accompanied driving in an environment that includes a companion and an obstacle includes a sensor, processing circuitry, and a driver. The sensor may generate sensor data. The processing circuitry may define a current state of the autonomous driving apparatus based on processing the sensor data to determine respective positions of the companion and the obstacle in the environment and select a first tracking point of a plurality of tracking points at least partially surrounding the position of the companion in the environment based on the current state, a position of each tracking point of the plurality of tracking points in the environment defined by the position of the companion in the environment. The driving apparatus drive mechanism may move the autonomous driving apparatus to the first tracking point to cause the autonomous driving apparatus to accompany the companion in the environment.

A cleanup support system that supports a cleanup behavior includes: a first obtaining unit configured to obtain first information indicating a level of interest of a target person in cleanup; a second obtaining unit configured to obtain second information indicating a level of achievement of the cleanup performed by the target person; a determination unit configured to determine a content of control corresponding to the first information obtained and the second information obtained, with reference to a rule which associates the level of interest in the cleanup and the level of achievement of the cleanup with a content of control performed on a device; and a control unit configured to control the device according to the content of control determined.

The present disclosure provides methods and systems for performing non-classical computations. The methods and systems generally use a plurality of spatially distinct optical trapping sites to trap a plurality of atoms, one or more electromagnetic delivery units to apply electromagnetic energy to one or more atoms of the plurality to induce the atoms to adopt one or more superposition states of a first atomic state and a second atomic state, one or more entanglement units to quantum mechanically entangle at least a subset of the one or more atoms in the one or more superposition states with at least another atom of the plurality, and one or more readout optical units to perform measurements of the superposition states to obtain the non-classical computation.

The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm. The invention can provide load dispatch scheme suitable for current microgrid and reduce operating cost of residential microgrid.

The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm. The invention can provide load dispatch scheme suitable for current microgrid and reduce operating cost of residential microgrid.

A reinforcement learning method and recognition apparatus includes: obtaining a structure graph, where the structure graph includes structure information that is of an environment or the intelligent agent and that is obtained through learning; inputing a current state of the environment and the structure graph to a policy function of the intelligent agent, where the policy function is used to generate an action in response to the current state and the structure graph, and the policy function of the intelligent agent is a graph neural network; outputing the action to the environment by using the intelligent agent; obtaining, from the environment by using the intelligent agent, a next state and reward data in response to the action; training the intelligent agent through reinforcement learning based on the reward data.

[Problem] To provide an automatic driving robot control device and control method that enable a vehicle to be operated smoothly while also being caused to conform to a command vehicle speed with high accuracy.

[Solution] The present invention provides an automatic driving robot (drive robot) 4 control device 10 that controls the automatic driving robot 4, which is installed in a vehicle 2 and causes the vehicle 2 to run, such that the vehicle 2 runs in accordance with a defined command vehicle speed, wherein the automatic driving robot 4 control device 10 is provided with: a running state acquisition unit 22 that acquires a running state of the vehicle 2 including a vehicle speed and the command vehicle speed; an operation content inference unit 31 that infers, on the basis of the running state, an operation sequence, which is a sequence of operations of the vehicle 2 at a plurality of times in the future that causes the vehicle 2 to run in accordance with the command vehicle speed, by using an operation inference learning model 40 that was trained by machine learning to infer the operation sequence; and a vehicle operation control unit 23 that extracts, from each of the operation sequences inferred a plurality of times in the past, the operations corresponding to a control time for subsequently controlling the automatic driving robot 4, calculates a weighted sum of these extracted plurality of operations to calculate a final operation value, generates, on the basis of the final operation value, a control signal for controlling the automatic driving robot 4, and transmits the control signal to the automatic driving robot 4.

A method includes causing at least a portion of a knowledge graph representing ontological health related information to be presented on a display of a client device. The method further includes receiving, at an artificial intelligence engine, a medical query, wherein the medical query includes a plurality of strings of characters. The method further includes identifying, in the plurality of strings of characters, indicia comprising a phrase, a predicate, a keyword, a subject, an object, a cardinal, a number, a concept, or some combination thereof. The method further includes comparing the indicia to the knowledge graph to generate an answer responsive to the medical query. The method further includes causing the answer to be presented at the client device.

Provided is a learning device including: a data acquisition unit configured to acquire, before control of a control target provided in equipment by a machine learning model that outputs an action corresponding to a state of the equipment, initialization data including state data indicating the state of the equipment and action data indicating an action on the control target; and a preliminary learning unit configured to initialize the machine learning model by performing preliminary learning on the basis of the initialization data before start of reinforcement learning corresponding to the control of the control target by the machine learning model.

Techniques are described for implementing an automated control system to control operations of a target physical system, such as production of electrical power in an electrical grid. The techniques may include determining how much electrical power for each of multiple producers to supply for each of a series of time periods, such as to satisfy projected demand for that time period while maximizing one or more indicated goals, and initiating corresponding control actions. The techniques may further include repeatedly performing automated modifications to the control system's ongoing operations to improve the target system's functionality, by using reinforcement learning to iteratively optimize particles generated for a time period that represent different state information within the target system, to learn one or more possible solutions for satisfying projected electrical power load during that time period while best meeting the one or more defined goals.