There is provided an information processing apparatus and information processing method that enables a user to easily teach an action with regard to action learning. A touchscreen outputs unknown state element information that indicates an unknown state element, and teaching request information that requests teaching of an action corresponding to an ambient state, in a case where the ambient state includes the unknown state element. For example, it is possible to apply the present disclosure to a cleaner robot or the like that controls actions on the basis of an action model for finding a probability P (a|s) that the cleaner robot performs an action a in a state s.

Provided is a machine learning device configured to perform machine learning related to optimization of a compensation value of a compensation generation unit with respect to a servo control device configured to control a servo motor configured to drive an axis of a machine tool, a robot, or an industrial machine, and that includes at least one feedback loop, a compensation generation unit configured to generate a compensation value to be applied to the feedback loop, and an abnormality detection unit configured to detect an abnormal operation of the servo motor, wherein, during a machine learning operation, when the abnormality detection unit detects an abnormality, the compensation from the compensation generation unit is stopped and the machine learning device continues optimization of the compensation value generated by the compensation generation unit.

A method and an apparatus for exclusive reinforcement learning are provided, comprising: collecting information of states of an environment through the communication interface and performing a statistical analysis on the states using the collected information; determining a first state value of a first state among the states in a training phase and a second state value of a second state among the states in an inference phase based on analysis results of the statistical analysis; performing reinforcement learning by using one reinforcement learning unit of a plurality of reinforcement learning unit which performs reinforcement learnings from different perspectives according to the first state value; and selecting one of actions determined by the plurality of reinforcement learning unit based on the second state value and applying selected action to the environment.

A method includes providing, to a trained machine learning model configured to determine a recommended adjustment to an equipment constant of a substrate manufacturing system, first input data indicative of a state of the substrate manufacturing system. The method further includes providing, to the trained machine learning model as second input data, an indication of a performed adjustment to the equipment constant. The method further includes retraining the trained machine learning model based on a difference between the recommended adjustment to the equipment constant and the performed adjustment to the equipment constant to generate a retrained machine learning model.

A system for manufacturing a semiconductor device includes a main system controller, a sub-system controller, and a process module. The main system controller provides a process recipe for manufacturing the semiconductor device and step identification information indicating one of a plurality of operations in the process recipe. The sub-system controller sets a process control variable based on the process recipe and the step identification information received from the main system controller. The process module perform the process recipe based on an input value determined by the process control variable

A machine learning device includes a data extraction unit that extracts first and second parameters from a plurality of machining programs. The machining programs numerically control a machine tool. The first parameter is a parameter to be adjusted, and the second parameter is a parameter used to adjust the first parameter. The machine learning device also includes a machine learning unit that learns a value of the first parameter according to a data set that includes the first and second parameters.

A computer implemented method for the maintenance optimization of a fleet or group of turbomachinery assets is disclosed. The method comprises the step of model training and setup, aiming at setting configurations parameters, that can be executed offline, and the step of online calculation on new input data, which is based on detected data and extracted statistical features. An anomaly identification and classification follow, thus calculating a risk assessment, for estimating the risk that an anomaly might cause any event that requires a maintenance task to be executed on one or more assets of the fleet.

An apparatus for printing an energy balance formulation, wherein the apparatus includes at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive an energy quantifier related to a user and generate an energy rebalancing plan wherein the energy rebalancing plan identifies an energy balance formulation includes training a machine-learning process using energy training data, wherein the energy training data contains a plurality of inputs containing energy quantifiers correlated to a plurality of outputs containing energy rebalancing plans. The memory contains instructions further configuring the processor to generate the energy rebalancing plan as a function of the machine-learning process and the energy quantifier. The memory contains instructions further configuring the additive manufacturing device to print the energy balance formulation based on the energy rebalancing plan.

An apparatus for printing an energy balance formulation, wherein the apparatus includes at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive an energy quantifier related to a user and generate an energy rebalancing plan wherein the energy rebalancing plan identifies an energy balance formulation includes training a machine-learning process using energy training data, wherein the energy training data contains a plurality of inputs containing energy quantifiers correlated to a plurality of outputs containing energy rebalancing plans. The memory contains instructions further configuring the processor to generate the energy rebalancing plan as a function of the machine-learning process and the energy quantifier. The memory contains instructions further configuring the additive manufacturing device to print the energy balance formulation based on the energy rebalancing plan.

A system may identify anomalous output among a plurality of outputs at a process step in the manufacturing process. A system may receive manufacturing attributes associated with each of the plurality of outputs including the anomalous output. A system may build, using a machine learning model, at least one isolation tree model comprising a plurality of parent nodes each corresponding to a split condition of one of the manufacturing attributes and a leaf node corresponding to the anomalous output, wherein each of the parent nodes of the at least one isolation tree model is associated with a manufacturing attribute directly leading to the anomalous output.