Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4948922 | Robotics and Autonomous Systems | 2016 | 11 Pages |
Abstract
These developments resonate with advances in machine learning, in particular hierarchical and modular approaches, as the field of artificial intelligence aims for general solutions to problems that typically rely on creativity in humans or other animals. We draw a parallel between the properties of insight according to psychology and the properties of Hierarchical Reinforcement Learning (HRL) systems for embodied agents. Using the Creative Systems Framework developed by Wiggins and Ritchie, we analyze both insight and HRL, establishing that they are creative in similar ways. We highlight the key challenges to be met in order to call an artificial system “insightful”.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Thomas R. Colin, Tony Belpaeme, Angelo Cangelosi, Nikolas Hemion,