Abstract: A computer-implemented method is provided of allowing a user to automatically transform domain knowledge into a machine learning model to be used in real-time operation of video games. The method comprises providing a user interface which allows a user to define domain knowledge relating to a video game by specifying one or more labeling functions; transforming the labeling functions into executable code; labeling raw data relating to the video game using the executable code to obtain labeled data; and applying an automated machine learning module to the labeled data to obtain a machine learning model.

Abstract: A collusion detection system may detect collusion between entities participating in online gaming. The collusion detection system may identify a plurality of entities associated with and opponents within an instance of an online game, determine social data associated with the plurality of entities, determine in-game behavior data associated with the plurality of entities, and determine, for one or more pairings of the plurality of entities, respective pairwise feature sets based at least in part on the social data and the in-game behavior data. The collusion detection system may then perform anomaly detection on the respective pairwise feature sets and, in response to the anomaly detection detecting one or more anomalous pairwise feature sets, output one or more suspect pairings of the plurality of entities corresponding to the one or more anomalous pairwise feature sets as suspected colluding pairings.

Abstract: Various aspects of the subject technology relate to systems, methods, and machine-readable media for granting access to a game. The method includes receiving a request to access the game. The method also includes causing to display a set of challenge options representing challenges of varying difficulty levels for a user to select. The method also includes receiving a selection of a challenge option from a user device. The method also includes causing to present a challenge to the user. The method also includes receiving challenge data from the user including interactions with and results of the challenge. The method also includes determining whether the user passed the challenge based on the challenge data. The method also includes executing or enabling execution of the game upon determining that the user passed the challenge. The method also includes providing the user rewards associated with the game for passing the challenge.

Abstract: A persona system determines a player persona for a player of a gaming system based on gameplay information for the user and, for example, performs dynamic content generation or additional product recommendations based on the player persona. The persona system may receive a request for content based on a persona of a player and receive gameplay data associated with gameplay of the player in a plurality of games. The persona system may then generate a player persona of the player based on the gameplay data associated with the gameplay of the player in the plurality of games, determine persona based content based at least in part on a portion of the player persona, and output the persona based content in response to the request.

Abstract: A system and method for providing live gameplay updates receives a modification for a videogame, the modification affecting a gameplay aspect of the videogame during execution of the videogame. The system and method determine a target group for deploying the modification. The target group includes first one or more live instances of the videogame. The system and method deploy the modification to the target group and receive gameplay data associated with the gameplay aspect from the target group. The system and method deploy the modification to second one or more live instances of the videogame based at least in part on an analysis of the received gameplay data.

Abstract: A persona system determines a player persona for a player of a gaming system based on gameplay information for the user and, for example, performs dynamic content generation or additional product recommendations based on the player persona. The persona system may receive a request for content based on a persona of a player and receive gameplay data associated with gameplay of the player in a plurality of games. The persona system may then generate a player persona of the player based on the gameplay data associated with the gameplay of the player in the plurality of games, determine persona based content based at least in part on a portion of the player persona, and output the persona based content in response to the request.

Abstract: A persona system determines a player persona for a player of a gaming system based on gameplay information for the user and, for example, performs dynamic content generation or additional product recommendations based on the player persona. The persona system may receive a request for content based on a persona of a player and receive gameplay data associated with gameplay of the player in a plurality of games. The persona system may then generate a player persona of the player based on the gameplay data associated with the gameplay of the player in the plurality of games, determine persona based content based at least in part on a portion of the player persona, and output the persona based content in response to the request.

Abstract: A videogame metrics query system, and related method, has one or more databases and a speculative cache. The system stores videogame metrics and tracks queries relating to videogame metrics. The system generates multiple queries, based on a received query and tracked queries. The system generates a combined query that has greater computational efficiency of execution. From executing the combined query, the system extracts query results relevant to the received query, and caches remaining results in the speculative cache.

Abstract: A computer-implemented method for providing video game content is provided. The method comprises monitoring a request rate of requests to provide video game content; and in response to the request rate exceeding a threshold request rate: initialising at least one instance of a first machine learning model, wherein the first machine learning model is configured to provide an output which is approximate to the output of a second machine learning model from which the first machine learning model is derived, the first machine learning model being produced by a model derivation process to have a faster response time compared to the second machine learning model; and providing video game content, wherein providing the video game content comprises generating an output responsive to the specified input using the at least one instance of the first machine learning model.

Abstract: A computer-implemented method is provided of allowing a user to automatically transform domain knowledge into a machine learning model to be used in real-time operation of video games. The method comprises providing a user interface which allows a user to define domain knowledge relating to a video game by specifying one or more labeling functions; transforming the labeling functions into executable code; labeling raw data relating to the video game using the executable code to obtain labeled data; and applying an automated machine learning module to the labeled data to obtain a machine learning model.

Abstract: A computer-implemented method for providing video game content is provided. The method comprises monitoring a request rate of requests to provide video game content; and in response to the request rate exceeding a threshold request rate: initialising at least one instance of a first machine learning model, wherein the first machine learning model is configured to provide an output which is approximate to the output of a second machine learning model from which the first machine learning model is derived, the first machine learning model being produced by a model derivation process to have a faster response time compared to the second machine learning model; and providing video game content, wherein providing the video game content comprises generating an output responsive to the specified input using the at least one instance of the first machine learning model.

Abstract: A computer-implemented method for providing video game content is provided. The method comprises maintaining a current machine learning model for each of a plurality of machine learning model branches; receiving a request to provide video game content responsive to specified input; in response to receiving the request, identifying a selected one of the machine learning model branches, wherein the machine learning model branch is selected based on an evaluation of the current machine learning model for each branch; and providing video game content responsive to the request, wherein providing the video game content comprises generating an output responsive to the specified input with the current machine learning model for the selected branch.

Abstract: Various aspects of the subject technology relate to systems, methods, and machine-readable media for granting access to a game. The method includes receiving a request to access the game. The method also includes causing to display a set of challenge options representing challenges of varying difficulty levels for a user to select. The method also includes receiving a selection of a challenge option from a user device. The method also includes causing to present a challenge to the user. The method also includes receiving challenge data from the user including interactions with and results of the challenge. The method also includes determining whether the user passed the challenge based on the challenge data. The method also includes executing or enabling execution of the game upon determining that the user passed the challenge. The method also includes providing the user rewards associated with the game for passing the challenge.

Abstract: Systems presented herein may automatically and dynamically modify a video game being played by a user based at least in part on a determined or predicted emotional state of a user. Using one or more machine learning algorithms, a parameter function can be generated that uses sensory and/or biometric data obtained by monitoring a user playing a video game. Based on the sensory and/or biometric data, an emotional state of the user can be predicted. For example, it can be determined whether a user is likely to be bored, happy, or frightened while playing the video game. Based at least in part on the determination of the user's emotional state, the video game can be modified to improve positive feelings and reduce negative feelings occurring in response to the video game.

Abstract: A system can manage distribution of computing jobs among a plurality of third-party network or cloud computing providers to maximize utilization of available computing resources purchased or otherwise obtained by an entity. The system can determine a dependency relationship between jobs and distribute the jobs among the network computing providers based at least in part on the dependency relationship between the jobs. Moreover, the system can use machine learning algorithms to generate one or more prediction algorithms to predict future computing resource usage demands for performing a set of scheduled and unscheduled jobs.

Abstract: As provided herein, a primary content provider (e.g., a book retailer) may present a campaign for a product (e.g., a novel), comprising non-published content and a keyword (e.g., the novel title), to a permitted content provider (e.g., the publisher of the novel). The permitted content provider may be presented with an option to bid on the keyword. Responsive to the bid being accepted by the primary content provider, a cumulative bid is determined based upon an accumulation of an existing bid from the primary content provider and the bid. The non-published content, but not second non-published content of a second primary content provider, may be selected to be provided to a user based upon the cumulative bid exceeding a second bid for the keyword by the second primary content provider. In this way, content that may be relevant to the user may be identified and provided to the user.

Abstract: Systems presented herein may automatically and dynamically modify a video game being played by a user based at least in part on a determined or predicted emotional state of a user. Using one or more machine learning algorithms, a parameter function can be generated that uses sensory and/or biometric data obtained by monitoring a user playing a video game. Based on the sensory and/or biometric data, an emotional state of the user can be predicted. For example, it can be determined whether a user is likely to be bored, happy, or frightened while playing the video game. Based at least in part on the determination of the user's emotional state, the video game can be modified to improve positive feelings and reduce negative feelings occurring in response to the video game.

Abstract: The present teaching relates to managing online experiments. In one example, a plurality of experiment layers is created with respect to a plurality of online users. Each experiment layer includes at least one experiment each of which includes one or more buckets associated with respective features to be experimented on. Each of the plurality of online users is assigned to a corresponding bucket in each experiment layer, such that the user is simultaneously associated with multiple experiments in different layers. User event data related to the plurality of experiment layers are collected from the plurality of online users. One or more contaminated buckets are automatically detected based on the user event data.

Abstract: Software on a content-aggregation website obtains a resource associated with a podcast from a website publishing the podcast and stores it e resource on the content-aggregation website. The software adds the resource as a leaf node to a taxonomy generated by the content-aggregation website. The addition is based on data associated with the podcast. The non-leaf nodes in the taxonomy are categories of content. The software determines that a user of the content-aggregation website is qualified as to at least one category that includes the resource as a leaf node. The determination is based at least in part on feedback from the user that includes a viewing or listening history for the user. Then the software serves the resource to the user in a content stream published by the content-aggregation website, based at least in part on a personalization score associated with the resource.

Abstract: As provided herein, a primary content provider (e.g., a book retailer) may present a campaign for a product (e.g., a novel), comprising non-published content and a keyword (e.g., the novel title), to a permitted content provider (e.g., the publisher of the novel). The permitted content provider may be presented with an option to bid on the keyword. Responsive to the bid being accepted by the primary content provider, a cumulative bid is determined based upon an accumulation of an existing bid from the primary content provider and the bid. The non-published content, but not second non-published content of a second primary content provider, may be selected to be provided to a user based upon the cumulative bid exceeding a second bid for the keyword by the second primary content provider. In this way, content that may be relevant to the user may be identified and provided to the user.