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.

Methods, systems, and computer storage media for providing an optimal control configuration for a material processing system are provided. In operation, a material processing engine accesses causal graph input data. Causal graph input data includes input data of a continuous flow process. Based on the causal graph and the input data, a causal graph that aligns with do-calculus manipulations—associated with determining identifiable causal relationships corresponding to input materials of the continuous flow process—is generated. The causal graph is parsed based on the do-calculus manipulations to determine valid conditioning sets associated with estimating a causal impact on an optimization target. Based on the valid conditioning sets, an optimal control configuration comprising optimal control variable values is generated. Generating the optimal control configuration comprising the optimal control variable values associated with the continuous flow process is based on solving a deterministic convex optimization problem and a corresponding stochastic optimization problem.

A computer system provides intelligent context-based control of air flow. An identity of a user and a location of the user within an area that includes one or more vents associated with a climate control system are identified. User preferences are determined based on the identity of the user. Characteristics of air flow in the area are adjusted based on the user preferences the user and the location of the user, wherein the characteristics of the air flow comprise one or more from a group of: a direction, and a flow rate. Embodiments of the present invention further include a method and program product for providing intelligent context-based control of air flow in substantially the same manner described above.

A warm-up evaluation device includes: a temperature data acquisition unit that acquires temperature data before warm-up operation when a machine performs the warm-up operation; a parameter value acquisition unit that acquires parameter values set in a program for performing the warm-up operation; an evaluation data acquisition unit that acquires evaluation data for evaluating a result of the warm-up operation; a learning unit that learns a machine learning model which receives the temperature data and the parameter values as an input and outputs the evaluation data on the basis of a plurality of warm-up operations performed by the same or the same types of machines; and an evaluation unit that inputs candidates for the parameter values to the machine learning model together with the temperature data and outputs the evaluation data when the same or the same types of machines perform a new warm-up operation.

An assistive system according to an embodiment of the present disclosure includes a cradle module including a holder body on which a user terminal is to be placed and a drive unit configured to drive the holder body, the cradle module configured to output a terminal connection signal when the user terminal is placed on the holder body, and an assistant server configured to receive the terminal connection signal from the cradle module and execute an assistive service, the assistant server configured to control the drive unit by a preset drive pattern through a drive control signal when executing the assistive service. It is possible to easily and conveniently identify a plurality of assistive services outputted according to each situation by controlling a cradle module through a drive pattern corresponding to an assistive service while outputting a voice corresponding to the assistive service when executing the assistive service.

Provided are methods for vehicle scenario mining for machine learning methods, which can include determining a set of attributes associated with an untested scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the untested scenario based on the set of attributes. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the untested scenario from the scenario database for inputting into the machine learning model for training the machine learning model. The machine learning model is configured to make the planned movements for the autonomous vehicle. Systems and computer program products are also provided.

Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.

A layer configuration prediction method is provided and includes: a specimen production step of producing multiple specimens by depositing layers of a material in configurations different from each other; a specimen measurement step of performing, on each specimen, measurement to acquire a texture parameter corresponding to a texture; a learning step of causing a computer to perform machine learning of a relation between each of the specimens and the texture parameter; a setting parameter calculation step of calculating a setting parameter corresponding to the texture set to a computer graphics image; and a layer configuration acquisition step of providing the setting parameter as an input to the computer having been caused to perform the machine learning, and acquiring an output representing the layering pattern of layers of the material corresponding to the setting parameter.

Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

A fixture that includes an electro-mechanical (EM) element; a communication interface; a processor; and a computer-readable storage media coupled to the processor and having instructions stored thereon which, when executed by the processor, cause the processor to perform operations comprising: receiving, from a service via the communication interface, parameters for scheduling an operation of the fixture; determining, based on the parameters, a plurality of commands for the EM element and a respective time to execute each of the commands; providing, at the respective time, each of the commands to the respective EM element for execution; providing, via the communication interface, an indication of completion of the operation of the fixture to the service.