Title

Machines That Learn And Teach Seamlessly

Keywords

augmented feedback; haptic feedback; intelligent tutoring systems; learning agents; Machine learning; psychomotor skill learning; teaching agents

Abstract

This paper describes an investigation into creating agents that can learn how to perform a task by observing an expert, then seamlessly turn around and teach the same task to a less proficient person. These agents are taught through observation of expert performance and thereafter refined through unsupervised practice of the task, all on a simulated environment. A less proficient human is subsequently taught by the now-trained agent through a third approach-coaching, executed through a haptic device. This approach addresses tasks that involve complex psychomotor skills. A machine-learning algorithm called PIGEON is used to teach the agents. A prototype is built and then tested on a task involving the manipulation of a crane to move large container boxes in a simulated shipyard. Two evaluations were performed-a proficiency test and a learning rate test. These tests were designed to determine whether this approach improves the human learning more than self-experimentation by the human. While the test results do not conclusively show that our approach provides improvement over self-learning, some positive aspects of the results suggest great potential for this approach. © 2008-2011 IEEE.

Publication Date

10-1-2013

Publication Title

IEEE Transactions on Learning Technologies

Volume

6

Issue

4

Number of Pages

389-402

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TLT.2013.32

Socpus ID

84890457803 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84890457803

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