Aptima, Inc.

Alan Carlin

Leah Swanson

Yale Marc

Danielle Dumond

E Webb Stacy

Sylvain Bruni

Michael Garrity

Courtney Dean

Tiffany Poeppelman

Andy Chang

Stephanie Pratt

Kristy Reynolds

Michael Keeney

Gilbert Mizrahi

In one embodiment of the invention, a training model for students is provided that models how to present training items to students in a computer based adaptive trainer. The training model receives student performance data and uses the training model to infer underlying student skill levels throughout the training sequence. Some embodiments of the training model also comprise machine learning techniques that allow the training model to adapt to changes in students skills as the student performs on training items presented by the training model. Furthermore, the training model may also be used to inform a training optimization model, or a learning model, in the form of a Partially Observable Markov Decision Process (POMDP).

Systems and methods for automated learning

Machine learning system for a training model of an adaptive trainer

Systems and methods to provide a training solution for a trainee are disclosed. In some embodiments the method comprises receiving a training requirement comprising a training outcome and a training configuration wherein the training configuration defines a trainee state, determining a training environment based on a relevancy function of the training environment to the training outcome, determining a training content based on a relationship function of the training content to the trainee state and determining a training solution comprising the training environment and the training content. In some embodiments, the relationship function comprises a POMDP model and the relevancy function comprises a best fit curve.

Systems and methods to provide training guidance

A computer implemented systems and methods for determining an action for a user within a learning domain are disclosed, some embodiments of the methods comprise defining an initial learning model of a learning domain, determining an initial user state of the user, determining an initial user action from at least one learning domain action with the initial learning model, receiving a user observation of the user after the user executes the initial user action, determining an updated user state with the initial learning model given the updated user observation and determining a subsequent user action from the at least one learning domain action. Some embodiments utilize a Partially Observable Markov Model (POMDP) as the learning model.

Systems and methods to customize student instruction

Computer implemented systems and methods of communicating a system reaction to environmental input comprising receiving environmental input, determining a hazard state and a user state from the environmental input, determining a system reaction from the hazard state and the user state and communicating the system reaction to a user interface. In some embodiments, the system reaction comprises a system reaction level and in some embodiments the system reaction level corresponds to a stage of automation. In some embodiments, the user interface is a multimodal interface and in some embodiments the user interface is a haptic interface.

Systems and methods to react to environmental input

Growing community of inventors

Arlington, MA, United States of America

Alan Carlin