Analyzing trajectories of learning processes through behaviour-based entropy

• Published in: Journal of experimental & theoretical artificial intelligence 2020-05, Vol.32 (3), p.465-501
• Main Authors: Seino, Shinsuke, Kimura, Kenji, Kawamura, Satoshi, Sasaki, Yoshifumi, Maruoka, Akira
• Format: Article
• Language: English
• Subjects: Behaviour-based entropy; Computer simulation; developmental learning processes; effort curve; Entropy; intuition vs. reasoning; Learning; learning curve; Learning curves; Maze learning; skill acquisition; Trajectory analysis
• ISSN: 0952-813X; 1362-3079
• DOI: 10.1080/0952813X.2019.1652358

Over recent decades, it has been asserted that the essence of developmental learning processes is change through learning. However, capturing the essence of change in learning processes remains an open question. To study learning processes, we take up a maze problem and conduct an experiment in which a participant draws a route for the maze problem over 10 sessions. To understand how a participant learns to draw a route, we draw learning curves by plotting, for each session, the number of mazes for which a participant succeeds in drawing correct routes. To analyze the learning process, we introduce a new metric called behaviour-based entropy, which quantifies the extent of how intensively a participant is devoted to drawing a route. A crucial finding is that substantial improvement in performance is preceded by a few sessions (plateau) during which the behaviour-based entropy is quite high. We run a program that simulates drawing of routes, and thereby obtain a learning curve based on the simulation. The resultant learning curves turn out to coincide roughly with the corresponding learning curves based on the experiment, which demonstrates the plausibility of the computational model for the simulation.