Probabilistic decision making system and methods of use
AssigneesWright State University, Aptima, Inc.
InventorsGeorgiy Levchuk, Jared Freeman, Wayne Shebilske
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Abstract
Embodiments of this invention comprise modeling a subject's state and the influence of training treatments, or actions, on that state to create a training policy. Both state and effects of actions are modeled as probabilistic using Partially Observable Markov Decision Process (POMDP) techniques. Utilizing this model and the resulting training policy with subjects creates an effective decision aid for instructors to improve learning relative to a traditional scenario selection strategy.
Claims20
A computer based system for determining training treatments for a subject, said system comprising:
- a memory to store at least one action comprising at least one training treatment;
- a processor capable of executing machine instructions;
- and
- the machine instructions configured to execute a POMDP model to create a training policy to determine the at least one training treatment to train a subject on a topic.
A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform the method steps comprising:
- generating a decision making policy from a POMDP model;
- the POMDP model comprising at least one state parameter, at least one observation parameter and at least one action parameter;
- and
- the action parameter comprising training treatments.
A computer based method for structuring training treatments for a subject on a topic, said method comprising:
- defining at least one action comprising at least one training treatment;
- and
- utilizing a POMDP model to create a training policy to determine the at least one training treatment to train a subject on a topic.
Dependencies are inferred from the claim text.