Abstract: Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

Abstract: In example embodiments, a method of interactive imitation learning method is disclosed. An input is received from an input device. The input includes data describing a first set of example actions defining a behavior for a virtual character. Inverse reinforcement learning is used to estimate a reward function for the set of example actions. The reward function and the set of example actions is used as input to a reinforcement learning model to train a machine learning agent to mimic the behavior in a training environment. Based on a measure of failure of the training of the machine learning agent reaching a threshold, the training of the machine learning agent is paused to request a second set of example actions from the input device. The second set of example actions is used in addition to the first set of example actions to estimate a new reward function.

Abstract: Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

Abstract: The present disclosure provides a system that automatically analyzes telemetric data, biometric data, and other data associated with a gameplay session to identify events occurring during the gameplay session. The telemetric data is generated by the game application during the gameplay session. The biometric data can be generated by input devices and can generate data associated with the user. The system can be configured to identify the segments associated with recorded gameplay events from the gameplay session and use the gameplay data associated with the events to create and output video data for a gameplay segment.

Abstract: In example embodiments, a method of interactive imitation learning method is disclosed. An input is received from an input device. The input includes data describing a first set of example actions defining a behavior for a virtual character. Inverse reinforcement learning is used to estimate a reward function for the set of example actions. The reward function and the set of example actions is used as input to a reinforcement learning model to train a machine learning agent to mimic the behavior in a training environment. Based on a measure of failure of the training of the machine learning agent reaching a threshold, the training of the machine learning agent is paused to request a second set of example actions from the input device. The second set of example actions is used in addition to the first set of example actions to estimate a new reward function.

Abstract: Embodiments of the systems and methods described herein can automatically measure the difficulty metrics associated with various aspects of a video game using an artificial intelligence system. The artificial intelligence system may include multiple game agents. Telemetry data associated with the gameplay of each game agent may be recorded while the game application is automatically executed by the game agents. The telemetry data may be communicated to a data analysis system which can calculate game difficulty metrics for various aspects of the game. The data analysis system can determine game difficulty associated with the various aspects based on the game difficulty metrics. The results from the data analysis system may be visualized and communicated to a game developer for updating the operations of the video game.

Abstract: Embodiments presented herein include systems and methods for performing dynamic difficulty adjustment. Further, embodiments disclosed herein perform dynamic difficulty adjustment using processes that may not be detectable or are more difficult to detect by users compared to static and/or existing difficulty adjustment processes. In some embodiments, historical user information utilized by a machine learning system to generate a prediction model that predicts an expected duration of game play, such as for example, an expected churn rate, a retention rate, the length of time a user is expected to play the game, or an indication of the user's expected game play time relative to a historical set of users who have previously played the game. Before or during game play, the prediction model can be applied to information about the user to predict the user's expected duration of game play.

Abstract: Embodiments of systems presented herein may perform automatic granular difficulty adjustment. In some embodiments, the difficulty adjustment is undetectable by a user. Further, embodiments of systems disclosed herein can review historical user activity data with respect to one or more video games to generate a game retention prediction model that predicts an indication of an expected duration of game play. The game retention prediction model may be applied to a user's activity data to determine an indication of the user's expected duration of game play. Based on the determined expected duration of game play, the difficulty level of the video game may be automatically adjusted.

Abstract: In example embodiments, a method of interactive imitation learning method is disclosed. An input is received from an input device. The input includes data describing a first set of example actions defining a behavior for a virtual character. Inverse reinforcement learning is used to estimate a reward function for the set of example actions. The reward function and the set of example actions is used as input to a reinforcement learning model to train a machine learning agent to mimic the behavior in a training environment. Based on a measure of failure of the training of the machine learning agent reaching a threshold, the training of the machine learning agent is paused to request a second set of example actions from the input device. The second set of example actions is used in addition to the first set of example actions to estimate a new reward function.

Abstract: Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

Abstract: The present disclosure provides embodiments of a virtual mapping system for using real world geographical locations to generate virtual environments within game applications. The virtual mapping system allows a game application to identify geographical information associated with real world locations. The geographical information can be used by the game application to recreate the selected geographical location using virtual assets from the game application.

Abstract: The present disclosure provides a system that automatically analyzes telemetric data, biometric data, and other data associated with a gameplay session to identify events occurring during the gameplay session. The telemetric data is generated by the game application during the gameplay session. The biometric data can be generated by input devices and can generate data associated with the user. The system can be configured to identify the segments associated with recorded gameplay events from the gameplay session and use the gameplay data associated with the events to create and output video data for a gameplay segment.

Abstract: Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

Abstract: Embodiments of the present disclosure provide a tutorial system that can aid a user in performing various game commands in response to different game states in a virtual game environment. As the user plays the game, various game states may be encountered. A tutorial engine may, based on a current game state, determine one or more game commands to be recommended to the user, based on historical information of the user and a game state model, wherein the game state model maintains associations between game states and different segments of users. The user is recommended relevant game commands during the normal course of gameplay, based on their own gameplay history and on game commands commonly performed by other users of the game application.

Abstract: Embodiments of systems presented herein may identify users to include in a match plan. A parameter model may be generated to predict the retention time of a set of users. A queue of potential users, a set of teammates, and/or opponents may be selected from a queue of waiting users. User information for the set of teammates and/or opponents may be provided to the parameter model to generate a predicted retention time. The set of teammates and/or opponents may be approved if the predicted retention time meets a predetermined threshold. Advantageously, by creating a match plan based on retention rates, the engagement and/or retention level for a number of users may be improved compared to existing multiplayer matching systems.

Abstract: Embodiments presented herein use an audio based authentication system for pairing a user account with an audio-based periphery computing system. The audio-based authentication system allows a user to interface with the periphery device through a user computing device. The user can utilize a previously authenticated user account on the user computing device in order to facilitate the pairing of the audio-based periphery computing system with the user account.

Abstract: The present disclosure provides a system that automatically analyzes telemetric data, biometric data, and other data associated with a gameplay session to identify events occurring during the gameplay session. The telemetric data is generated by the game application during the gameplay session. The biometric data can be generated by input devices and can generate data associated with the user. The system can be configured to identify the segments associated with recorded gameplay events from the gameplay session and use the gameplay data associated with the events to create and output video data for a gameplay segment.

Abstract: Using voice recognition, a user can interact with a companion application to control a video game from a mobile device. Advantageously, the user can interact with the companion application when the video game is unavailable because, for example, of the user's location. Moreover, machine learning may be used to facilitate generating voice responses to user utterances that are predicted to improve or maintain a user's level of engagement with the companion application, or its corresponding video game.

Abstract: Embodiments of the systems and methods described herein can automatically measure the difficulty metrics associated with various aspects of a video game using an artificial intelligence system. The artificial intelligence system may include multiple game agents. Telemetry data associated with the gameplay of each game agent may be recorded while the game application is automatically executed by the game agents. The telemetry data may be communicated to a data analysis system which can calculate game difficulty metrics for various aspects of the game. The data analysis system can determine game difficulty associated with the various aspects based on the game difficulty metrics. The results from the data analysis system may be visualized and communicated to a game developer for updating the operations of the video game.

Abstract: A method for evaluating a build of a computer-implemented game is disclosed. An evaluation request is received. The evaluation. request includes an identification of the build, data describing one or more behaviors for the build, and data describing one or more tests. One or more simulations of playing of the build are performed using the one or more behaviors. One or more metrics ate extracted from the simulations. Each of the one or more metrics measures an aspect of the computer-implemented game. One or more tests are applied to the one or more metrics to evaluate an adherence of the build to the one or more tests. A display of the evaluation is caused to be displayed in a user interface of a client device.