Optimal data-driven decision-making in multi-agent systems

A method comprising:

• receiving constant data associated with a human user;
• receiving input data associated with adjusting one or more control parameters of an exoskeleton device operated by the human user;
• generating training data by incremental changes of the one or more control parameters and by:
• receiving, from one or more sensors associated with the exoskeleton device, first data associated with metrics of the human user;
• and
• receiving, from the one or more sensors associated with the exoskeleton device, second data associated with motor input associated with actions of the human user,
• generating, based at least in part on the training data, a conjectural model, wherein the conjectural model predicts a responding action of the human user in response to a change of a first control parameter;
• determining, using the conjectural model, a first predicted action based at least in part on the change of the first control parameter;
• receiving, from the one or more sensors, observed data associated with the metrics of the human user and the motor input associated with the actions of the human user;
• determining a rate of error associated with the first predicted action and the observed data;
• generating, based at least in part on the observed data and the rate of error, an updated conjectural model;
• and
• returning to receiving the observed data from the one or more sensors.

A system comprising:

• one or more processors;
• a memory;
• and
• one or more components stored in the memory and executable by the one or more processors to perform operations comprising:
• receiving an output objective associated with an area domain;
• receiving a one or more models associated with determining an action for a first agent to predict a reaction from a second agent;
• determining, using the one or more models, a first action for the first agent to cause a first reaction from the second agent based at least in part on the output objective;
• receiving, from the first agent, observed data associated with the second agent;
• determining a rate of error associated with the first reaction and the observed data;
• and
• determining, using the one or more models and based at least in part on the rate of error and the output objective, a second action for the first agent to cause a second reaction from the second agent.

A method comprising:

• receiving, from first agents, training data associated with first observations of an environment and reactions associated with second agents;
• generating, based at least in part on the training data, one or more machine learning (ML) models, wherein the one or more ML models comprises a value world model and a conjectural model associated with the second agents, the conjectural model being configured to receive input observation data and output conjectural responses for the second agents;
• receiving, from a first agent of the first agents, data associated with second observations of the environment, wherein a portion of the data is associated with responses of a second agent of the second agents;
• determining, based at least in part on the data, an event probability of an environment state associated with the environment and a conjectural probability of a response associated with the second agent;
• determining, based at least in part on the event probability of the environment state and the conjectural probability of the response, an updated value world model and an updated conjectural model;
• determining, based at least in part on the updated value world model and the updated conjectural model, an action for the first agent;
• receiving, from the first agent, an observed response of the second agent;
• and
• returning to receiving the data from the first agent.

A method comprising:

• performing a conjectural process with an objective associated with a first agent, the conjectural process comprising:
• receiving, from the first agent, data associated with observations of an environment, wherein the environment comprises a second agent;
• determining, based at least in part on the data, conjectural probabilities for predicted responses associated with the second agent;
• determining, based at least in part on the conjectural probabilities, an objective response from the predicted responses;
• determining, based at least in part on the objective response, at least one action of possible actions that anticipate the objective response;
• determining, based at least in part on the at least one action, an action for the first agent;
• and
• receiving, from the first agent, an observed response of the second agent;
• and
• repeating the conjectural process until the objective associated with the first agent is achieved.

A method comprising:

• receiving input data, the input data comprising an initial condition, a predetermined conjectural equilibrium (CE) tolerance, an initial depth level, a performance criteria, and a depth level tolerance;
• initiating conjecture variables with the input data, the conjecture variables comprising a current depth level initiated to the initial depth level, a first action profile associated with a first agent, and a second action profile associated with a second agent;
• repeating a CE process while the current depth level is less than the depth level tolerance and while a joint action profile between the first action profile and the second action profile is less than the predetermined CE tolerance, the CE process comprising:
• determining, by using a machine model and the current depth level, the joint action profile between the first action profile and the second action profile;
• computing the performance criteria based at least in part on the predetermined CE tolerance and the current depth level;
• storing the performance criteria in an array associated with the current depth level;
• and
• increasing the current depth level incrementally;
• determining a ranking for the performance criteria in the array;
• and
• determining an optimal depth level is associated with a first ranked performance criteria in the array.

A method comprising:

• collecting data associated with observations of a world, wherein the world comprises a first agent and a second agent;
• determining, by applying a model to the data, estimated world state data, wherein determining the estimated world state data comprises:
• identifying one or more time-invariant world metrics;
• and
• generating an updated value model associated with the world;
• determining, by applying the model to the data, estimated opponent action data, wherein determining the estimated opponent action data comprises:
• identifying one or more time-varying action metrics associated with the second agent;
• and
• generating an updated conjectural model associated with the second agent.