This specification describes a computer-implemented method of generating an intent-based model for use in selecting actions in a video game. The method comprises initializing a graph comprising a plurality of nodes. Each node of the plurality of nodes represents a state of an entity in the video game. The method further comprises adding one or more edges to the graph. Each edge of the one or more edges represents a transition from a first state to a second state. The method further comprises determining, for each node of the plurality of nodes, a distance to each other node, comprising performing a path-finding algorithm on the graph. The method further comprises determining one or more outcome nodes. Each outcome node represents an outcome state of the entity. The method further comprises scoring the one or more outcome nodes, comprising, for each outcome node, determining a score based on an outcome of the outcome node. The method further comprises scoring the plurality of nodes of the graph. Scoring the plurality of nodes of the graph comprises, for each node of the plurality of nodes, and for each outcome out of a set of outcomes, determining whether one or more outcome nodes for the outcome are immediately available from the node; and when one or more outcome nodes for the outcome are immediately available from the node, scoring the outcome for the node using the scores of the one or more outcome nodes. The method further comprises, for each node of the graph, and for each outcome out of the set of outcomes, determining a distance from the node to a highest scoring outcome node for the outcome.

This specification describes a computer-implemented method of generating an intent-based model for use in selecting actions in a video game. The method comprises initializing a graph comprising a plurality of nodes. Each node of the plurality of nodes represents a state of an entity in the video game. The method further comprises adding one or more edges to the graph. Each edge of the one or more edges represents a transition from a first state to a second state. The method further comprises determining, for each node of the plurality of nodes, a distance to each other node, comprising performing a path-finding algorithm on the graph. The method further comprises determining one or more outcome nodes. Each outcome node represents an outcome state of the entity. The method further comprises scoring the one or more outcome nodes, comprising, for each outcome node, determining a score based on an outcome of the outcome node. The method further comprises scoring the plurality of nodes of the graph. Scoring the plurality of nodes of the graph comprises, for each node of the plurality of nodes, and for each outcome out of a set of outcomes, determining whether one or more outcome nodes for the outcome are immediately available from the node; and when one or more outcome nodes for the outcome are immediately available from the node, scoring the outcome for the node using the scores of the one or more outcome nodes. The method further comprises, for each node of the graph, and for each outcome out of the set of outcomes, determining a distance from the node to a highest scoring outcome node for the outcome.

Provided is a method of automatically creating an agent that may interact with a user in a video game and providing a pre-created agent upon request of the user playing the video game when there is no user to play the video game.

Provided is a method of automatically creating an agent that may interact with a user in a video game and providing a pre-created agent upon request of the user playing the video game when there is no user to play the video game.

An automated video game testing framework and method includes communicatively coupling an application programming interface (API) to an agent in a video game, where the video game includes a plurality of in-game objects that are native to the video game. The agent is managed as an in-game object of the video game. A test script is executed to control the agent, via the API, to induce gameplay and interrogate a behavior of a test object. The test object is identified from the plurality of in-game objects based on a query that specifies an object attribute of the test object.

An automated video game testing framework and method includes communicatively coupling an application programming interface (API) to an agent in a video game, where the video game includes a plurality of in-game objects that are native to the video game. The agent is managed as an in-game object of the video game. A test script is executed to control the agent, via the API, to induce gameplay and interrogate a behavior of a test object. The test object is identified from the plurality of in-game objects based on a query that specifies an object attribute of the test object.

In various examples, potentially highlight-worthy video clips are identified from a gameplay session that a gamer might then selectively share or store for later viewing. The video clips may be identified in an unsupervised manner based on analyzing game data for durations of predicted interest. A classification model may be trained in an unsupervised manner to classify those video clips without requiring manual labeling of game-specific image or audio data. The gamer can select the video clips as highlights (e.g., to share on social media, store in a highlight reel, etc.). The classification model may be updated and improved based on new video clips, such as by creating new video-clip classes.

In various examples, potentially highlight-worthy video clips are identified from a gameplay session that a gamer might then selectively share or store for later viewing. The video clips may be identified in an unsupervised manner based on analyzing game data for durations of predicted interest. A classification model may be trained in an unsupervised manner to classify those video clips without requiring manual labeling of game-specific image or audio data. The gamer can select the video clips as highlights (e.g., to share on social media, store in a highlight reel, etc.). The classification model may be updated and improved based on new video clips, such as by creating new video-clip classes.

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.

The present disclosure refers to a method and a system for synthesizing videogame assets, the method includes receiving a first set of videogame assets as input in a videogame asset synthesizer. The videogame asset synthesizer may identify certain common features among the assets of the first set of videogame assets and clusters the videogame assets into different asset profiles based on the certain common features identified. The videogame asset synthesizer may synthesize a subset of asset candidates for an asset profile, based on the videogame assets clustered into the asset profile and rate the asset candidates synthesized, based on a threshold set for at least one parameter of the asset candidates. The assets may be stored in a database, based on the rating.