DEEP CAUSAL LEARNING FOR ADVANCED MODEL PREDICTIVE CONTROL

    公开(公告)号:US20230060325A1

    公开(公告)日:2023-03-02

    申请号:US17797940

    申请日:2021-02-19

    Abstract: Method for predictive control of a system having subsystems. The method includes providing signal injections relating to performance of the system. The signal injections include various operational controls for the system or its subsystems. Response signals corresponding with the signal injections are received, and a utility of those signals is measured. Based upon the utility of the response signals, data relating to operational controls is modified to optimize performance of the system via its subsystems.

    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS

    公开(公告)号:US20220146988A1

    公开(公告)日:2022-05-12

    申请号:US17438677

    申请日:2019-09-11

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes identifying a procedural instance; determining a temporal extent for the procedural instance based on temporal extent parameters for the one or more entities in the procedural instance; selecting control settings for the procedural instance; monitoring environment responses to the control settings that are received for the one or more entities; determining which of the environment responses to attribute to the procedural instance in a causal model; and adjusting, based at least in part on the environment responses that are attributed to the procedural instance, the temporal extent parameters for the one or more entities.

    DETERMINING CAUSAL MODELS FOR CONTROLLING ENVIRONMENTS

    公开(公告)号:US20220128955A1

    公开(公告)日:2022-04-28

    申请号:US17438725

    申请日:2019-09-11

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes repeatedly selecting, by a control system for the environment, control settings for the environment based on internal parameters of the control system, wherein: at least some of the control settings for the environment are selected based on a causal model, and the internal parameters include a first set of internal parameters that define a number of previously received performance metric values that are used to generate the causal model for a particular controllable element; obtaining, for each selected control setting, a performance metric value; determining that generating the causal model for the particular controllable element would result in higher system performance; and adjusting, based on the determining, the first set of internal parameters.

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