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Intrinsically Motivated Machines.

Authors :
Carbonell, Jaime G.
Siekmann, Jörg
Lungarella, Max
Iida, Fumiya
Bongard, Josh
Pfeifer, Rolf
Kaplan, Frédéric
Oudeyer, Pierre-Yves
Source :
50 Years of Artificial Intelligence; 2007, p303-314, 12p
Publication Year :
2007

Abstract

Children seem intrinsically motivated to manipulate, to explore, to test, to learn and they look for activities and situations that provide such learning opportunities. Inspired by research in developmental psychology and neuroscience, some researchers have started to address the problem of designing intrinsic motivation systems. A robot controlled by such systems is able to autonomously explore its environment not to fulfil predefined tasks but driven by an incentive to search for situations where learning happens efficiently. In this paper, we present the origins of these intrinsically motivated machines, our own research in this novel field and we argue that intrinsic motivation might be a crucial step towards machines capable of life-long learning and open-ended development. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540772958
Database :
Complementary Index
Journal :
50 Years of Artificial Intelligence
Publication Type :
Book
Accession number :
33412751
Full Text :
https://doi.org/10.1007/978-3-540-77296-5_27