Abstract: Disclosed is a method performed by a computing device for implementing a Graphical User Interface (GUI) providing a development environment, the method including: setting, by a computing device, a plurality of code blocks; designating two or more execution target blocks among the plurality of code blocks; constructing one or more pipelines defining a relationship between the two or more execution target blocks and connecting the two or more execution target blocks; and executing at least some of the two or more execution target blocks based on the connection relationship of the one or more pipelines.

Abstract: Disclosed is a method for predicting areas of environmental information needed to be collected, which is performed by one or more processors of a computing device. The method may include: outputting one or more episodes based on environmental information; measuring uncertainty for each of the one or more episodes; and predicting an area of the environmental information needed to be collected based on the measured uncertainty.

Abstract: Disclosed is a method for controlling an air conditioning device, which is performed by at least one computing device, which includes: determining a control action for the air conditioning device at a first time point by using a reinforcement learning agent; determining a reward for the control action at the first time point based on a reward delay time by using the reinforcement learning agent; and performing reinforcement learning related to the control of the air conditioning device based on the determined reward, in which a time point when the reward delay time elapses from the first time point corresponds to a second time point, and the reward for the control action at the first time point is calculated while excluding situations after the first time point and before the second time point.

Abstract: Disclosed is a method for distributing work points to a plurality of task-performing robots, the method performed by one or more processors of a computing device. The method may include: determining available work points for each of the plurality of task-performing robots; distributing target work points for each of the plurality of task-performing robots based on the determined available work points, and predicting a plurality of target work trajectories for each of the plurality of task-performing robots based on the distributed target work points; calculating a first loss or a second loss based on the predicted plurality of target work trajectories; and re-distributing target work points to each of the plurality of task-performing robots based on at least one of the calculated first loss or the calculated second loss.

Abstract: Disclosed is a method for providing a development environment. Specifically, according to the present disclosure, a computing device identifies a plurality of components included in an entire pipeline, recommends an execution environment in which each component is to be executed based on information on each of the plurality of components; and executes the plurality of components based on the recommendation, and the execution environment includes a remote execution environment.

Abstract: Disclosed is a method for adjusting a parameter of a reinforcement learning algorithm, is performed by a computing device. Specifically, according to the present disclosure, the computing device extracts at least some of episodes of the reinforcement learning algorithm, determines a complexity of a task performed by the reinforcement learning algorithm based on at least some episodes, and adjusts a parameter of the reinforcement learning algorithm based on the complexity.

Abstract: Disclosed is a method for calculating a work trajectory of a task-performing robot, the method performed by one or more processor of a computing device. The method may include: determining available work points of a task-performing robot; generating plurality of candidate work trajectories for the task-performing robot based on the determined available work points; and predicting a distance between the determined available work points based on the generated plurality of candidate work trajectories in order to distribute a target work point to the task-performing robot.

Abstract: Disclosed is a method performed by a computing device to generate a working trajectory of an industrial robot. Particularly, the computing device according to the present disclosure identifies a plurality of working points for one or more industrial robots; identifies a collision point based on a trajectory between the plurality of working points; modifies the trajectory between the working points based on information of the collision point; and generates a working trajectory of the industrial robot based on the modified trajectory between the working points.

Abstract: Disclosed is a method of generating a program for a robotic process, the method being performed by a computing device, the method including: identifying valid working spots for a robot based on analyzing a pose of the robot; distributing one or more target working spots to the robot, based on estimating a distance between the valid working spots of the robot; and determining a work trajectory or work sequence of the robot in consideration of a possibility of collision of the robot.

Abstract: Disclosed is a method of determining a valid working spot of a robot, the method performed by a computing device, the method including: identifying a working spot; determining whether a robot is capable of taking at least one pose for working the working spot; when the robot is capable of taking at least one pose for working the working spot, determining the working spot as a valid working spot of the robot; and generating a search space for distributing working spots based on the valid working spot of the robot.

Abstract: Disclosed is a method for preventing a collision between a plurality of task-performing robots, the method performed by one or more processors of a computing device.

Abstract: Disclosed is a method for placing a semiconductor cell, which is performed by a computing device, and the method may include: determining a placement location of a macro cell in a design area using a reinforcement learning model; determining a candidate direction for shifting the placement location of the macro cell determined by the reinforcement learning model; and shifting the placement location of the macro cell based on the determined candidate direction, and the determined candidate direction may include a direction facing an outside of the design area.

Abstract: Disclosed is a method of performing double clustering to evaluate placement of semiconductor devices performed by a computing device according to an exemplary embodiment of the present disclosure. The method includes receiving connection relationship information representing a connection relationship between semiconductor devices, perform clustering on the semiconductor devices by utilizing first reference information based on the connection relationship information, and perform sub-clustering in a cluster generated by the clustering, by utilizing second reference information based on the connection relationship information.

Abstract: Disclosed is a method for predicting areas of environmental information needed to be collected, which is performed by one or more processors of a computing device. The method may include: outputting one or more episodes based on environmental information; measuring uncertainty for each of the one or more episodes; and predicting an area of the environmental information needed to be collected based on the measured uncertainty.

Abstract: Disclosed is a method for controlling an air conditioning device, which is performed by at least one computing device, which includes: determining a control action for the air conditioning device at a first time point by using a reinforcement learning agent; determining a reward for the control action at the first time point based on a reward delay time by using the reinforcement learning agent; and performing reinforcement learning related to the control of the air conditioning device based on the determined reward, in which a time point when the reward delay time elapses from the first time point corresponds to a second time point, and the reward for the control action at the first time point is calculated while excluding situations after the first time point and before the second time point.

Abstract: Disclosed is a method for control simulation based on artificial intelligence according to an exemplary embodiment of the present disclosure. Specifically, according to the present disclosure, a computing device obtains a first state information, a second state information, and a control information, and generates first output information based on the first state information, the second state information, and the control information by using an artificial neural network model including a sequential neural network. In this case, the first output information includes one or more output variables, and at least some of the one or more output variables correspond to variables included in the second state information, and the first output information is generated based on applying an attention mechanism to each of the one or more output variables.

Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, in which when the computer program is executed by one or more processors of a computing device, the computer program performs operations to provide methods for detecting flaws, and the operations may include: extracting a flaw patch from a flaw image including a flaw; preprocessing at least one of the flaw image or non-flaw image not including a flaw; extracting a non-flaw patch from at least one of the preprocessed flaw image or non-flaw image; and training a neural network model for classifying patches to flaw or non-flaw with a training data set including the flaw patch and the non-flaw patch.

Abstract: Disclosed is a method of evaluating placement of semiconductor devices performed by a computing device according to an exemplary embodiment of the present disclosure. The method includes receiving connection information representing a connection relationship between semiconductor devices; clustering the semiconductor devices based on the connection information; and determining a reward to train a neural network model based on clustering.

Abstract: The disclosed technology generally relates to novelty detection and more particularly to novelty detection methods using a deep learning neural network and apparatuses and non-transitory computer-readable media configured for performing the methods. In one aspect, a method for detecting novelty using a deep learning neural network model comprises providing a deep learning neural network model. The deep learning neural network model comprises an encoder comprising a plurality of encoder layers and a decoder comprising a plurality of decoder layers. The method additionally comprises feeding a first input into the encoder and successively processing the first input through the plurality of encoder layers to generate a first encoded input, wherein successively processing the first input comprises generating a first intermediate encoded input from one of the encoder layers prior to generating the first encoded input.

Abstract: An anomaly data detecting method performed by a computing device having at least one processor includes acquiring first time-series data, dividing the first time-series data into a plurality of sub time-series data, adjusting scales of variable values included in at least one sub time-series data among the plurality of sub time-series data and determining whether the first time-series data is abnormal by inputting scaled first time-series data to a neural network based detection model.