Active Learning using Adaptive Curiosity
Alexis Bondu, Vincent Lemaire · 2006
Exploratory activities seems to be crucial for our cognitive development. According to spychologists, exploration is an intrinsically rewarding behaviour. That explains the autonomous and active development of children. The developmental robotics aim to design computational systems that are endowed with such an intrinsic motivation mechanism. There are possible links between developmental robotics and classical machine learning. Active learning strategies aim to the most informative examples and adaptive curiosity allows a robot to explore its environement in an intelligente way. In this article, the adaptive curiosity framework is reformulated in terms of active learning terminology, and compared directly to existing algorithms in this field. The main contribution of this article is a new criterion evaluating the potential interestingness of zones of the sensorimotor space.