Machines that learn and teach seamlessly
Gary Stein, Avelino J. González, Clayton Barham · IEEE Transactions on Learning Technologies · 2013
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.