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Interactive Task Learning: Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions

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Humans are not limited to a fixed set of innate or preprogrammed tasks. We learn quickly through language and other forms of natural interaction, and we improve our performance and teach others what we have learned. Understanding the mechanisms that underlie the acquisition of new tasks through natural interaction is an ongoing challenge. Advances in artificial intelligence, cognitive science, and robotics are leading us to future systems with human-like capabilities. A huge gap exists, however, between the highly specialized niche capabilities of current machine learning systems and the generality, flexibility, and in situ robustness of human instruction and learning. Drawing on expertise from multiple disciplines, this Strüngmann Forum Report explores how humans and artificial agents can quickly learn completely new tasks through natural interactions with each other.

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Publisher

: The MIT Press., 2019

Collation

1 online resource

Language

English

ISBN/ISSN

9780262349420

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NONE

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Laird

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Tedy

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Digital Object Identifier (DOI)

https://doi.org/10.7551/mitpress/11956.001.0001

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