Interactive task learning: humans, robots, and agents acquiring new tasks through natural interactions<br/> 

 - Cambridge  MIT Press  2019 
 - xiii, 337p.  23cm. 
 - Strüngmann Forum Reports .
<br/><br/>Includes Bibliography and Subject Index 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9780262038829 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Human-computer interaction<br/>Machine learning<br/>Robotics--Human factors<br/>Artificial intelligence--Educational applications<br/>Computer-assisted instruction<br/>Intelligent tutoring systems<br/>Artificial intelligence
<br/><br/><label>Dewey Class. No.: </label>004.019  / GLU