Abstract: An apparatus for generating datasets for training machine learning models includes: a receiving unit configured to receive video data comprising sequential image frames; a storage unit configured to store a plurality of the sequential image frames; and a selecting unit configured to select, for a target image frame, a subset of stored image frames, the subset providing contextual data relating to the target image frame for the machine learning model. The selecting unit is configured to successively generate sampling values, wherein a difference between successive sampling values increases with each successively generated sampling value; and the selecting unit is configured to select a given image frame from the stored image frames in dependence upon whether a number of sequential image frames between the given image frame and the target image frame coincides with one of the successively generated sampling values.

Abstract: A system for training a model to select actions to be taken by an agent within an environment, the system including: a state determination unit operable to determine a state of the environment, a latency determination unit operable to determine a latency associated with interactions between the agent and the environment, an action determination unit operable to determine one or more actions to be performed by the agent in dependence upon the state, wherein actions are determined for each of one or more latencies determined by the latency determination unit, an action evaluation unit operable to evaluate the success of each of the actions, and a generation unit operable to generate the model in dependence upon identifying correlations between the success of each of the actions and the determined latency associated with those actions, so as to identify an action to be taken by the agent.

Abstract: A data processing apparatus comprising circuitry configured to: obtain gameplay data indicating in-game behaviour of a human video game player; configure, using the obtained gameplay data, an artificial intelligence, AI, agent representing the human video game player; execute one or more AI agent video game sessions including the AI agent and obtain AI agent performance data indicating in-game performance of the AI agent during the one or more AI agent video game sessions; and perform matchmaking of the human video game player with another human video game player based on the obtained AI agent performance data.

Abstract: A data processing apparatus for detecting one or more errors for one or more video images for a video game, the data processing apparatus comprising receiving circuitry to receive a sequence of video images for the video game and one or more action inputs associated with the sequence of video images, prediction circuitry to generate a predicted video image in dependence on at least one video image of the sequence of video images and an action input associated with the at least one video image, the prediction circuitry comprising a trained machine learning model to generate the predicted video image, and error detection circuitry to detect, for one or more video images subsequent to the at least one video image, one or more errors in dependence on the predicted video image.

Abstract: A content generation system, comprising: receiving circuitry configured to receive environment geometry data of at least part of a three-dimensional, 3D, virtual environment; selecting circuitry comprising a selecting model trained to select, based on the received environment geometry data, one or more virtual elements; and generating circuitry configured to instantiate the selected virtual elements within the at least part of the virtual environment.

Abstract: A gaming system, comprising: generating circuitry configured to generate, for a current gaming session, a virtual environment comprising a plurality of portions, wherein the generating circuitry is configured to instantiate a subset of a total number of elements of a first video game within a first portion of the virtual environment; receiving circuitry configured to receive, from a user input device, input signals indicating a user's level of engagement during the user's interaction with the first portion; and determining circuitry comprising a determining model trained to determine a second video game based on the first portion and the received input signals; wherein the generating circuitry is configured to instantiate a subset of a total number of elements of the second video game within a second portion of the virtual environment.

Abstract: A method of controlling at least a first non-player object within a virtual environment of an application includes: selecting a template non-player object behavioural AI model, the AI model being previously trained separately to the application using reinforcement learning to characterise behaviour of a respective non-player object type, from among a plurality of template AI models, associating one or more non-player objects of the application with the selected template AI model, and for the or each associated non-player object, inputting application state information to an input interface of the template AI model for receiving state information relevant to the associated non-player object's type, and receiving from an output interface of the template AI model non-player object state information, and then updating the state of the respective non-player object for the virtual environment of the application responsive to the received non-player object state information.

Abstract: An apparatus for generating datasets for training machine learning models includes: a receiving unit configured to receive video data comprising sequential image frames; a storage unit configured to store a plurality of the sequential image frames; and a selecting unit configured to select, for a target image frame, a subset of stored image frames, the subset providing contextual data relating to the target image frame for the machine learning model. The selecting unit is configured to successively generate sampling values, wherein a difference between successive sampling values increases with each successively generated sampling value; and the selecting unit is configured to select a given image frame from the stored image frames in dependence upon whether a number of sequential image frames between the given image frame and the target image frame coincides with one of the successively generated sampling values.

Abstract: A system for generating a training dataset for a machine learning process, and training a machine learning model, the system comprising a data obtaining unit configured to obtain training data comprising a plurality of events of interest and the behaviour of an agent corresponding to those events, an event identifying unit configured to identify, based upon one or more corresponding indicators, the occurrence of an event of interest in the training data, a list generating unit configured to generate a list of identified events in the training data, wherein identified events are added to the list with a probability that is inversely proportional to the frequency of the occurrence of that event within the training data, a dataset generating unit configured to generate a dataset comprising information about the events contained in the generated list, and a training unit configured to train a machine learning model using the generated dataset, wherein the machine learning model is trained to generate behaviour fo

Abstract: A data processing apparatus adapted to output recommendation information for modifying source code, includes: compiler circuitry to compile the source code and to output compiled code for the source code, processing circuitry to execute the compiled code, profile circuitry to monitor the execution of the compiled code by the processing circuitry and to generate profile information for the execution of the compiled code, the profile information including one or more statistical properties for the execution of the compiled code, and recommendation circuitry to output the recommendation information for the source code, the recommendation circuitry including a machine learning model to receive at least a portion of the profile information and trained to output the recommendation information for the source code in dependence upon one or more of the statistical properties, in which the recommendation information is indicative of one or more editing instructions for modifying the source code.

Abstract: A method of controlling at least a first non-player object within a virtual environment of an application includes: selecting a template non-player object behavioural AI model, the AI model being previously trained separately to the application using reinforcement learning to characterise behaviour of a respective non-player object type, from among a plurality of template AI models, associating one or more non-player objects of the application with the selected template AI model, and for the or each associated non-player object, inputting application state information to an input interface of the template AI model for receiving state information relevant to the associated non-player object's type, and receiving from an output interface of the template AI model non-player object state information, and then updating the state of the respective non-player object for the virtual environment of the application responsive to the received non-player object state information.

Abstract: A system for training a model to select actions to be taken by an agent within an environment, the system including: a state determination unit operable to determine a state of the environment, a latency determination unit operable to determine a latency associated with interactions between the agent and the environment, an action determination unit operable to determine one or more actions to be performed by the agent in dependence upon the state, wherein actions are determined for each of one or more latencies determined by the latency determination unit, an action evaluation unit operable to evaluate the success of each of the actions, and a generation unit operable to generate the model in dependence upon identifying correlations between the success of each of the actions and the determined latency associated with those actions, so as to identify an action to be taken by the agent in dependence upon both a determined state and a latency.

Abstract: A data processing apparatus adapted to output recommendation information for modifying source code, includes: compiler circuitry to compile the source code and to output compiled code for the source code, processing circuitry to execute the compiled code, profile circuitry to monitor the execution of the compiled code by the processing circuitry and to generate profile information for the execution of the compiled code, the profile information including one or more statistical properties for the execution of the compiled code, and recommendation circuitry to output the recommendation information for the source code, the recommendation circuitry including a machine learning model to receive at least a portion of the profile information and trained to output the recommendation information for the source code in dependence upon one or more of the statistical properties, in which the recommendation information is indicative of one or more editing instructions for modifying the source code.