Authors:
Sanghun Bang
and
Charles Tijus
Affiliation:
Laboratoire Cognitions Humaine et Artificielle (CHArt), University Paris 8, 2 rue de la Liberté, 93526 Saint-Denis and France
Keyword(s):
Problem Solving, Neural Network, Recurrent Neural Network, Reinforcement Learning, Cognition, Embodied Cognition, Tower of Hanoi.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Systems
;
Pattern Recognition
;
Reinforcement Learning
;
Soft Computing
;
Soft Computing and Intelligent Agents
;
Theory and Methods
Abstract:
Models of puzzle problem solving, such as Tower of Hanoi, are based on moves analysis. In a grounded and embodied based approach of cognition, we thought that gestures made to take the discs to one place and place them in another place could be beneficial to the learning process, as well as to the modeling and simulation. Gestures comprise moves, but in addition they are also prerequisites of moves when the free hand goes in one location to take a disc. Our hypothesis is that we can model the solving of the Tower of Hanoi through observing the actions of the hand with and without objects. We collected sequential data of moves and gestures of participants solving the Tower of Hanoi with four dicks and, then, train a Recurrent Neural Network model of Tower of Hanoi based on these data in order to find the shortest solution path. In this paper, we propose an approach for change of state sequences training, which combines Recurrent Neural Network and Reinforcement Learning methods.