Teaching by Intervention: Working Backwards, Undoing Mistakes, or Correcting Mistakes?

Mark K. Ho, Michael L. Littman, Joseph L. Austerweil

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When teaching, people often intentionally intervene on a learner while it is acting. For instance, a dog owner might move the dog so it eats out of the right bowl, or a coach might intervene while a tennis player is practicing to teach a skill. How do people teach by intervention? And how do these strategies interact with learning mechanisms? Here, we examine one global and two local strategies: working backwards from the end-goal of a task (backwards chaining), placing a learner in a previous state when an incorrect action was taken (undoing), or placing a learner in the state they would be in if they had taken the correct action (correcting). Depending on how the learner interprets an intervention, different teaching strategies result in better learning. We also examine how people teach by intervention in an interactive experiment and find a bias for using local strategies like undoing.

Original languageEnglish (US)
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages526-531
Number of pages6
ISBN (Electronic)9780991196760
StatePublished - 2017
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: Jul 26 2017Jul 29 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period7/26/177/29/17

Keywords

  • intervention
  • reinforcement learning
  • teaching

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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