System for probabilistic reasoning and decision making on digital twins

A system for creating digital twins, the system comprising:

• a memory;
• and
• one or more processors communicatively coupled to the memory, the one or more processors configured to:
• obtain a dataset, wherein the dataset comprises an ontology and domain data corresponding to a domain associated with the ontology;
• construct a knowledge graph based on the ontology;
• incorporate at least a portion of the domain data into the knowledge graph;
• convert the knowledge graph to a probabilistic graph model,
• wherein the probabilistic graph model represents a digital twin of a real world counterpart, wherein the real world counterpart comprises a robot that is used to unload trucks arriving at a loading dock,
• wherein the probabilistic graph model comprises a plurality of nodes and edges connecting at least some of the plurality of nodes to one or more other nodes,
• wherein the probabilistic graph model comprises probability distributions having unknown parameters associated with the plurality of nodes, and
• wherein the plurality of nodes include a node associated with a battery level percentage of the robot, a node associated with a set point indicating the battery level percentage at which the robot stops charging, a node associated with the amount of time the robot needs to charge to the set point and a node associated with a throughput specifying how many items per minute the robot can unload from the trucks;
• learn the unknown parameters based on the portion of the domain data;
• and
• update the probabilistic graph model based on the learning, wherein the unknown parameters are replaced with parameters obtained via the learning via the update, and wherein the update includes obtaining a joint probability distribution of the probabilistic graph model;
• generate a query associated with at least one random variable associated with the probability distributions of the probabilistic graph model using the joint probability distribution of the probabilistic graph model;
• run the query against the probabilistic graph model to obtain a query result, wherein the query result comprises a distribution associated with the at least one random variable;
• generate a control signal based on the query result, wherein the control signal specifies the battery level percentage for the set point to optimize the throughput of the robot;
• and
• transmit the control signal to the robot for optimal operation.

A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for generating probabilistic graph models, the operations comprising:

• obtaining a dataset, wherein the dataset comprises an ontology and domain data corresponding to a domain associated with the ontology;
• constructing a knowledge graph based on the ontology;
• incorporating at least a portion of the domain data into the knowledge graph;
• and
• converting the knowledge graph to a probabilistic graph model,
• wherein the probabilistic graph model represents a digital twin of a real world counterpart, wherein the real world counterpart comprises a robot that is used to unload trucks arriving at a loading dock,
• wherein the probabilistic graph model comprises a plurality of nodes and edges connecting at least some of the plurality of nodes to one or more other nodes,
• wherein the probabilistic graph model comprises probability distributions having unknown parameters associated with the plurality of nodes, and
• wherein the plurality of nodes include a node associated with a battery level percentage of the robot, a node associated with a set point indicating the battery level percentage at which the robot stops charging, a node associated with the amount of time the robot needs to charge to the set point and a node associated with a throughput specifying how many items per minute the robot can unload from the trucks;
• learning the unknown parameters based on the portion of the domain data;
• and
• updating the probabilistic graph model based on the learning, wherein the updating:
• replaces the unknown parameters with parameters obtained via the learning, and
• includes obtaining a joint probability distribution of the probabilistic graph model;
• generating a query associated with at least one random variable associated with the probability distributions of the probabilistic graph model using the joint probability distribution of the probabilistic graph model;
• running the query against the probabilistic graph model to obtain a query result, wherein the query result comprises a distribution associated with the at least one random variable;
• generating a control signal based on the query result, wherein the control signal specifies the battery level percentage for the set point to optimize the throughput of the robot;
• and
• transmitting the control signal to the robot for optimal operation.