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    DETECTING PERSONAL-SPACE VIOLATIONS IN ARTIFICIAL INTELLIGENCE BASED NON-PLAYER CHARACTERS
    1.
    发明公开
    DETECTING PERSONAL-SPACE VIOLATIONS IN ARTIFICIAL INTELLIGENCE BASED NON-PLAYER CHARACTERS 审中-公开

    公开(公告)号：US20230310995A1

    公开(公告)日：2023-10-05

    申请号：US17709904

    申请日：2022-03-31

    Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

    Inventor： Mehdi Saeedi ,  Ian Charles Colbert ,  Thomas Daniel Perry ,  Gabor Sines

    IPC: A63F13/56

    CPC classification number: A63F13/56

    Abstract: Systems, apparatuses, and methods for detecting personal-space violations in artificial intelligence (AI) based non-player characters (NPCs) are disclosed. An AI engine creates a NPC that accompanies and/or interacts with a player controlled by a user playing a video game. During gameplay, measures of context-dependent personal space around the player and/or one or more NPCs are generated. A control circuit monitors the movements of the NPC during gameplay and determines whether the NPC is adhering to or violating the measures of context-dependent personal space. The control circuit can monitor the movements of multiple NPCs simultaneously during gameplay, keeping a separate score for each NPC. After some amount of time has elapsed, the scores of the NPCs are recorded, and then the scores are provided to a machine learning engine to retrain the AI engines controlling the NPCs.

    JOB SCHEDULING USING REINFORCEMENT LEARNING
    2.
    发明公开
    JOB SCHEDULING USING REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20230154100A1

    公开(公告)日：2023-05-18

    申请号：US17529916

    申请日：2021-11-18

    Applicant: ADVANCED MICRO DEVICES, INC. ,  ATI TECHNOLOGIES ULC

    Inventor： Thomas Daniel Perry ,  Steven Tovey ,  Mehdi Saeedi ,  Andrej Zdravkovic ,  Zhuo Chen

    IPC: G06T15/00 ,  G06F9/48

    CPC classification number: G06T15/005 ,  G06F9/4881 ,  G06N20/00

    Abstract: Systems, methods, and techniques utilize reinforcement learning to efficiently schedule a sequence of jobs for execution by one or more processing threads. A first sequence of execution jobs associated with rendering a target frame of a sequence of frames is received. One or more reward metrics related to rendering the target frame are selected. A modified sequence of execution jobs for rendering the target frame is generated, such as by reordering the first sequence of execution jobs. The modified sequence is evaluated with respect to the selected reward metric(s); and rendering the target frame is initiated based at least in part on the evaluating of the modified sequence with respect to the one or more selected reward metric(s).

    UPDATING SHADER SCHEDULING POLICY AT RUNTIME
    4.
    发明公开
    UPDATING SHADER SCHEDULING POLICY AT RUNTIME 审中-公开

    公开(公告)号：US20230206537A1

    公开(公告)日：2023-06-29

    申请号：US17562884

    申请日：2021-12-27

    Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

    Inventor： Thomas Daniel Perry ,  Steven John Tovey ,  Mehdi Saeedi

    IPC: G06T15/00 ,  A63F13/52

    CPC classification number: G06T15/005 ,  A63F13/52

    Abstract: Systems, apparatuses, and methods for updating and optimizing task scheduling policies are disclosed. A new policy is obtained and updated at runtime by a client based on a server analyzing a wide spectrum of telemetry data on a relatively long time scale. Instead of only looking at the telemetry data from the client's execution of tasks for the previous frame, the server analyzes the execution times of tasks for multiple previous frames so as to determine a more optimal policy for subsequent frames. This mechanism enables making a more informed task scheduling policy decision as well as customizing the policy per application, game, and user without requiring a driver update. Also, this mechanism facilitates improved load balancing across the various processing engines, each of which has their own task queues. The improved load balancing is achieved by analyzing the telemetry data including resource utilization statistics for the different processing engines.

    Detecting personal-space violations in artificial intelligence based non-player characters
    8.
    发明授权
    Detecting personal-space violations in artificial intelligence based non-player characters 有权

    公开(公告)号：US12172081B2

    公开(公告)日：2024-12-24

    申请号：US17709904

    申请日：2022-03-31

    Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

    Inventor： Mehdi Saeedi ,  Ian Charles Colbert ,  Thomas Daniel Perry ,  Gabor Sines

    IPC: A63F13/56

    Abstract: Systems, apparatuses, and methods for detecting personal-space violations in artificial intelligence (AI) based non-player characters (NPCs) are disclosed. An AI engine creates a NPC that accompanies and/or interacts with a player controlled by a user playing a video game. During gameplay, measures of context-dependent personal space around the player and/or one or more NPCs are generated. A control circuit monitors the movements of the NPC during gameplay and determines whether the NPC is adhering to or violating the measures of context-dependent personal space. The control circuit can monitor the movements of multiple NPCs simultaneously during gameplay, keeping a separate score for each NPC. After some amount of time has elapsed, the scores of the NPCs are recorded, and then the scores are provided to a machine learning engine to retrain the AI engines controlling the NPCs.

    Adaptive audio mixing
    9.
    发明授权
    Adaptive audio mixing 有权

    公开(公告)号：US11839815B2

    公开(公告)日：2023-12-12

    申请号：US17132827

    申请日：2020-12-23

    Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

    Inventor： Carl Kittredge Wakeland ,  Mehdi Saeedi ,  Thomas Daniel Perry ,  Gabor Sines

    IPC: A63F13/67 ,  A63F13/79 ,  A63F13/54 ,  G06N3/08 ,  G06F16/635 ,  A63F13/428 ,  G06F3/01 ,  G11B27/02

    CPC classification number: A63F13/54 ,  A63F13/428 ,  G06F3/011 ,  G06F3/017 ,  G06N3/08 ,  G11B27/02 ,  A63F2300/105 ,  A63F2300/6081

    Abstract: Systems, apparatuses, and methods for performing adaptive audio mixing are disclosed. A trained neural network dynamically selects and mixes pre-recorded, human-composed music stems that are composed as mutually compatible sets. Stem and track selection, volume mixing, filtering, dynamic compression, acoustical/reverberant characteristics, segues, tempo, beat-matching and crossfading parameters generated by the neural network are inferred from the game scene characteristics and other dynamically changing factors. The trained neural network selects an artist's pre-recorded stems and mixes the stems in real-time in unique ways to dynamically adjust and modify background music based on factors such as game scenario, the unique storyline of the player, scene elements, the player's profile, interest, and performance, adjustments made to game controls (e.g., music volume), number of viewers, received comments, player's popularity, player's native language, player's presence, and/or other factors. The trained neural network creates unique music that dynamically varies according to real-time circumstances.

    HUMAN-LIKE NON-PLAYER CHARACTER BEHAVIOR WITH REINFORCEMENT LEARNING
    10.
    发明申请
    HUMAN-LIKE NON-PLAYER CHARACTER BEHAVIOR WITH REINFORCEMENT LEARNING 有权

    公开(公告)号：US20220309364A1

    公开(公告)日：2022-09-29

    申请号：US17215437

    申请日：2021-03-29

    Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

    Inventor： Thomas Daniel Perry ,  Mehdi Saeedi ,  Gabor Sines

    IPC: G06N5/04 ,  G06N20/00 ,  A63F13/56

    Abstract: Systems, apparatuses, and methods for creating human-like non-player character (NPC) behavior with reinforcement learning (RL) are disclosed. An artificial intelligence (AI) engine creates a NPC that has seamless movement when accompanying a player controlled by a user playing a video game. The AI engine is RL-trained to stay close to the player but not get in the player's way while acting in a human-like manner. Also, the AI engine is RL-trained to evaluate the quality of information that is received over time from other AI engines and then to act on the evaluated information quality. Each AI agent is trained to evaluate the other AI agents and determine whether another AI agent is a friend or a foe. In some cases, groups of AI agents collaborate together to either help or hinder the player. The capabilities of each AI agent are independent from the capabilities of other AI agents.

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