Interactive task learning: humans, robots, and agents acquiring new tasks through natural interactions

Contributor(s):

Series: Strüngmann Forum ReportsPublication details: MIT Press 2019 CambridgeDescription: xiii, 337p. 23cmISBN:

9780262038829

Subject(s):

DDC classification:

004.019 GLU

Summary: 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.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar General Stacks	General	004.019 GLU (Browse shelf(Opens below))	1	Available		031013

Browsing IIT Gandhinagar shelves, Shelving location: General Stacks, Collection: General Close shelf browser (Hides shelf browser)

Previous								Next
Previous	004.01 LEE Coevolution: the entwined futures of humans and machines	004.0151 SIP Introduction to the theory of computation	004.0151 SIP Introduction to the theory of computation	004.019 GLU Interactive task learning: humans, robots, and agents acquiring new tasks through natural interactions	004.019 LEV UX strategy : how to devise innovative digital products that people want	004.1 JON Quantum information, computation and communication	004.16 BAR Fundamentals of wearable computers and augmented reality	Next

Includes Bibliography and Subject Index

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.

There are no comments on this title.

to post a comment.