Strategies for Interactive Task Learning and Teaching

Katrien Beuls, Luc Steels, Paul Van Eecke

Research output: Chapter in Book/Report/Conference proceedingChapter


A strategy is a way to make decisions that come up when handling a task. It requires a problem solver able to address routine cases and a set of diagnostics and repairs to handle, in a flexible way, unusual or unforeseen situations. Between humans, interactive task learning and teaching appear to involve strategies at three levels: (a) the execution of a task with available knowledge (task strategy), (b) interactive learning to expand the available knowledge and thus become a better problem solver in the future (learning strategy), and (c) interactive teaching or tutoring to help others learn (teaching strategy). This chapter examines the general architecture that is needed to build artificial agents that can play either the role of teacher, by carrying out teaching strategies, or the role of learner, by carrying out learning strategies that benefit from these teaching strategies. Focus is on artificial teachers that interact with humans or artificial learners as well as artificial learners that interact with human or artificial teachers. We argue that the use of a meta-layer is of primary importance for understanding and implementing strategies and point to operational examples from an implementation of this hypothesis in the domain of second-language teaching.
Original languageEnglish
Title of host publicationInteractive Task Learning
Subtitle of host publicationHumans, Robots, and Agents Acquiring New Tasks through Natural Interactions
EditorsJohn E. Laird, Kevin A. Gluck
PublisherMIT Press
ISBN (Print)9780262038829
Publication statusPublished - Aug 2019

Publication series

NameStrüngmann Forum Reports
PublisherMIT Press


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