Curiosity and Interactive Learning in Artificial Systems

As “scientists in the crib,” children learn through curiosity, tirelessly seeking novelty and information as they interact—really, play—with both physical objects and the people around them. This flexible capacity to learn about the world through intrinsically motivated interaction continues throughout life. How would we engineer an artificial, autonomous agent that learns in this way – one that flexibly interacts with its environment, and others within it, in order to learn as humans do? In this chapter, I will first motivate this question by describing important advances in artificial intelligence in the last decade, noting ways in which artificial learning within these methods are and are not like human learning. I will then give an overview of recent results in artificial intelligence aimed at replicating curiosity-driven interactive learning. I will then close by speculating on how AI that learns in this fashion could be used as fine-grained computational models of human learning.

DOI: 10.1007/978-3-031-09687-7_3