Abstract: A method is implemented via a game analysis platform that includes at least one processor and at least one memory. The method includes: generating a training data set based on game data collected from actual players; training an artificial intelligence (AI) model using machine learning based on the training data set; gathering actual game data from game play; processing the actual game data via the AI model to generate detection results; and detecting a potential player bot or use of cheating software when detection results exceed a detection threshold.

Abstract: In various embodiments, a method is presented that includes: generating, via a system including a processor, a quality assurance (QA) game bot; receiving, via the system, a gaming application corresponding to a game; updating the gaming application, via the system, based on play of the game by the QA game bot to generate a first updated gaming application corresponding to a first updated game; receiving, via the system, game telemetry data of the first updated gaming application corresponding to actual players of the first updated game; updating, via the system, the QA gaming bot based on the game telemetry data of the first updated gaming application corresponding to actual players to generate an updated QA gaming bot; and updating the first updated gaming application, via the system, based on play of the first updated game by the updated QA game bot to generate a second updated gaming application corresponding to a second updated game.

Abstract: A method is implemented via a game analysis platform that includes at least one processor and at least one memory. The method includes: generating a training data set based on game data collected from actual players; training an artificial intelligence (AI) model using machine learning based on the training data set; gathering actual game data from game play; processing the actual game data via the AI model to generate detection results; and detecting a potential player bot or use of cheating software when detection results exceed a detection threshold.

Abstract: A system operates by generating BEA tools that include an AI model trained via machine learning based on prior game play; receiving game data and multimodal player data associated with a play of a gaming application by a player; generating a predicted user experience by applying the BEA tools to the game data and the multimodal player data, wherein the predicted user experience includes motivation data that indicates motivation scores for the player for each of a plurality of different motivations, and wherein each of the motivation scores for the player predicts an amount that the player is motivated by one of the plurality of different motivations while playing the gaming application; and facilitating adaptation of the gaming application based on the predicted user experience.

Abstract: An AI system is trained to operate by receiving game telemetry data from a gaming application associated with game play of a player, wherein the telemetry data includes pixel data from the gaming application; generating a predicted user motivation by applying the BEA tools to the game telemetry data, wherein the predicted user motivation includes motivation data that indicates motivation scores for the player for each of a plurality of motivation factors associated with a corresponding one of a plurality of different motivations, and wherein each of the motivation scores for the player predicts an amount that the player's behavior while playing the gaming application is motivated by one of the plurality of different motivations; and facilitating adaptation of the gaming application based on the predicted user motivation.

Abstract: An AI system is trained to operate by receiving game telemetry data from a gaming application associated with game play of a player, wherein the telemetry data includes pixel data from the gaming application; generating a predicted user motivation by applying the BEA tools to the game telemetry data, wherein the predicted user motivation includes motivation data that indicates motivation scores for the player for each of a plurality of motivation factors associated with a corresponding one of a plurality of different motivations, and wherein each of the motivation scores for the player predicts an amount that the player's behavior while playing the gaming application is motivated by one of the plurality of different motivations; and facilitating adaptation of the gaming application based on the predicted user motivation.

Abstract: A system operates by generating BEA tools that include an AI model trained via machine learning based on prior game play; receiving game data and multimodal player data associated with a play of a gaming application by a player that includes verbal data generated from the player and non-verbal data generated from the player; generating a predicted user experience by applying the BEA tools to the game data and the multimodal player data, wherein the predicted user experience includes motivation data that indicates motivation scores for the player for each of a plurality of different motivations, and wherein each of the motivation scores for the player predicts an amount that the player is motivated by one of the plurality of different motivations while playing the gaming application; and facilitating adaptation of the gaming application based on the predicted user experience.

Abstract: A system operates by generating BEA tools that include an AI model trained via machine learning based on prior game play; receiving game data and multimodal player data associated with a play of a gaming application by a player that includes verbal data generated from the player and non-verbal data generated from the player; generating a predicted user experience by applying the BEA tools to the game data and the multimodal player data, wherein the predicted user experience includes motivation data that indicates motivation scores for the player for each of a plurality of different motivations, and wherein each of the motivation scores for the player predicts an amount that the player is motivated by one of the plurality of different motivations while playing the gaming application; and facilitating adaptation of the gaming application based on the predicted user experience.

Abstract: A game development platform operates by: updating a gaming application corresponding to a game based on game play by at least one non-imitating game bot to generate a first updated gaming application corresponding to a first updated game; generating an imitating game bot based on first game telemetry data generated in response to play of the first updated game by actual players; updating the first updated gaming application based on play of the first updated game by the imitating game bot to generate a second updated gaming application corresponding to a second updated game; generating motivation data indicating predicted player motivations based on second game telemetry data generated in response to play of the second updated game; and updating the second updated gaming application based on the motivation data to generate a third updated gaming application corresponding to a third updated game.

Abstract: A procedural content generation tool operates by: generating, via image analysis, graphs of existing game content; generating a symmetrical Markov random field (SMRF) model based on the graphs; and automatically generating, via iterative artificial intelligence (AI), new game content based on the SMRF model.

Abstract: A method is implemented via a game analysis platform that includes at least one processor and at least one memory. The method includes: generating a training data set based on game data collected from actual players; training an artificial intelligence (AI) model using machine learning based on the training data set; gathering actual game data from game play; processing the actual game data via the AI model to generate detection results; and detecting a potential player bot or use of cheating software when detection results exceed a detection threshold.

Abstract: A game development platform operates by: updating a gaming application based on a play of the game by a non-imitating game bot to generate a first updated gaming application corresponding to a first updated game; generating an imitating game bot based on first game telemetry data generated in response to a play of the first updated game by a first plurality of actual players; generating behavioral experience analysis (BEA) data based on the play of the first updated game by the first plurality of actual players; generating a BEA tool based on the BEA data; updating the first updated gaming application based on play of the first updated game by the imitating game bot to generate a second updated gaming application corresponding to a second updated game; generating predicted player motivations via the BEA tool based on second game telemetry data generated in response to a play of the second updated game by a second plurality of actual players; and updating the second updated gaming application based on the pred

Abstract: A game development platform operates by: updating a gaming application based on a play of the game by a non-imitating game bot to generate a first updated gaming application corresponding to a first updated game; generating an imitating game bot based on first game telemetry data generated in response to a play of the first updated game by a first plurality of actual players; generating behavioral experience analysis (BEA) data based on the play of the first updated game by the first plurality of actual players; generating a BEA tool based on the BEA data; updating the first updated gaming application based on play of the first updated game by the imitating game bot to generate a second updated gaming application corresponding to a second updated game; generating predicted player motivations via the BEA tool based on second game telemetry data generated in response to a play of the second updated game by a second plurality of actual players; and updating the second updated gaming application based on the pred

Abstract: In various embodiments, a method is presented that includes: generating, via a system including a processor, behavioral experience analysis (BEA) tools based on a preference learning model; receiving, via the system, game telemetry data from a gaming application; generating, via the system, a predicted user motivation by applying the BEA tools to the game telemetry data; and facilitating adaptation of the gaming application based on the predicted user motivation.

Abstract: A game development platform operates by: updating a gaming application based on a play of the game by a non-imitating game bot to generate a first updated gaming application corresponding to a first updated game; generating an imitating game bot based on first game telemetry data generated in response to a play of the first updated game by a first plurality of actual players; generating behavioral experience analysis (BEA) data based on the play of the first updated game by the first plurality of actual players; generating a BEA tool based on the BEA data; updating the first updated gaming application based on play of the first updated game by the imitating game bot to generate a second updated gaming application corresponding to a second updated game; generating predicted player motivations via the BEA tool based on second game telemetry data generated in response to a play of the second updated game by a second plurality of actual players; and updating the second updated gaming application based on the pred

Abstract: A procedural content generation tool operates by: generating, via image analysis, graphs of existing game content; generating a symmetrical Markov random field (SMRF) model based on the graphs; and automatically generating, via iterative artificial intelligence (AI), new game content based on the SMRF model.

Abstract: In various embodiments, a method is presented that includes: generating, via a system including a processor, a gaming bot; receiving, via the system, game telemetry data of a gaming app corresponding to an actual player; generating, via the system, game telemetry data of the gaming app corresponding to the gaming bot; generating, via the system, difference data based on the game telemetry data corresponding to an actual player and the game telemetry data corresponding to the gaming bot, the difference data indicating a difference over time between a first character generated by the actual player and a second character generated by the gaming bot; and updating, via the system, the gaming bot based on the difference data.

Abstract: In various embodiments, a method is presented that includes: generating, via a system including a processor, a gaming bot; receiving, via the system, game telemetry data of a gaming app corresponding to an actual player; generating, via the system, game telemetry data of the gaming app corresponding to the gaming bot; generating, via the system, difference data based on the game telemetry data corresponding to an actual player and the game telemetry data corresponding to the gaming bot, the difference data indicating a difference over time between a first character generated by the actual player and a second character generated by the gaming bot; and updating, via the system, the gaming bot based on the difference data.

Abstract: In various embodiments, a method is presented that includes: generating, via a system including a processor, behavioral experience analysis (BEA) tools based on a preference learning model; receiving, via the system, game telemetry data from a gaming application; generating, via the system, a predicted user motivation by applying the BEA tools to the game telemetry data; and facilitating adaptation of the gaming application based on the predicted user motivation.