Abstract: System and methods for utilizing a video game console to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and collect game state data corresponding to how the player reacts to the particular gameplay situation or an effect of the reaction. In some cases, the video game console can receive an exploratory rule set to apply during the particular gameplay situation. In some cases, the video game console can trigger the particular gameplay situation. A system can receive the game state data from many video game consoles and train a rule set based on the game state data. Advantageously, the system can save computational resources by utilizing the players' video game experience to train the rule set.

Abstract: Systems and methods are disclosed for training a machine learning model to control an in-game character or other entity in a video game in a manner that aims to imitate how a particular player would control the character or entity. A generic behavior model that is trained without respect to the particular player may be obtained and then customized based on observed gameplay of the particular player. The customization training process may include freezing at least a subset of layers or levels in the generic model, then generating one or more additional layers or levels that are trained using gameplay data for the particular player.

Abstract: System and methods for utilizing a video game console to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and collect game state data corresponding to how the player reacts to the particular gameplay situation or an effect of the reaction. In some cases, the video game console can receive an exploratory rule set to apply during the particular gameplay situation. In some cases, the video game console can trigger the particular gameplay situation. A system can receive the game state data from many video game consoles and train a rule set based on the game state data. Advantageously, the system can save computational resources by utilizing the players' video game experience to train the rule set.

Abstract: Systems and methods are disclosed for training a machine learning model to control an in-game character or other entity in a video game in a manner that aims to imitate how a particular player would control the character or entity. A generic behavior model that is trained without respect to the particular player may be obtained and then customized based on observed gameplay of the particular player. The customization training process may include freezing at least a subset of layers or levels in the generic model, then generating one or more additional layers or levels that are trained using gameplay data for the particular player.

Abstract: Certain implementations of the disclosed technology may include methods and computing systems for memory-assisted compression of network packets using packet-level clustering. According to an example implementation, a method is provided. The method may include vectorizing a plurality of data packets stored in a memory, calculating respective distances between each of the respective vectorized data packets, clustering the plurality of data packets into a plurality of data packet clusters, obtaining a sample data packet to be compressed, identifying a training data packet cluster from among the plurality of data packet clusters, and compressing the sample data packet using a compression algorithm.

Abstract: Certain implementations of the disclosed technology may include methods and computing systems for memory-assisted compression of network packets using packet-level clustering. According to an example implementation, a method is provided. The method may include vectorizing a plurality of data packets stored in a memory, calculating respective distances between each of the respective vectorized data packets, clustering the plurality of data packets into a plurality of data packet clusters, obtaining a sample data packet to be compressed, identifying a training data packet cluster from among the plurality of data packet clusters, and compressing the sample data packet using a compression algorithm.

Abstract: Disclosed are embodiments of a compressed distributed storage system that is designed to satisfy: reliability; minimum storage; efficient update; cost-effective access. An exemplary system can comprise a splitter, an encoder, a parameterizer, and a compressor. In contrast to the prior art, the encoding is performed before the compression. Furthermore, in the exemplary system parameterization, data classification, and memory-assisted compression are key features in efficient compression. The splitter can split an input data file into a plurality of original segments. The encoder can perform fault-tolerant encoding on the plurality of original segments, providing plurality of redundant segments. The parameterizer can classify each redundant segment and form and memorize statistics (context) of each class of the redundant segments. With the class-based context, each redundant segment can be compressed and later decompressed individually.