%0 Journal Article
	%A Jianyong Xue and  Olivier L. Georgeon and  Salima Hassas
	%D 2020
	%J International Journal of Educational and Pedagogical Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 166, 2020
	%T A Constructivist Approach and Tool for Autonomous Agent Bottom-up Sequential Learning
	%U https://publications.waset.org/pdf/10011477
	%V 166
	%X During the initial phase of cognitive development,
infants exhibit amazing abilities to generate novel behaviors in
unfamiliar situations, and explore actively to learn the best while
lacking extrinsic rewards from the environment. These abilities
set them apart from even the most advanced autonomous robots.
This work seeks to contribute to understand and replicate some of
these abilities. We propose the Bottom-up hiErarchical sequential
Learning algorithm with Constructivist pAradigm (BEL-CA) to
design agents capable of learning autonomously and continuously
through interactions. The algorithm implements no assumption about
the semantics of input and output data. It does not rely upon a
model of the world given a priori in the form of a set of states
and transitions as well. Besides, we propose a toolkit to analyze the
learning process at run time called GAIT (Generating and Analyzing
Interaction Traces). We use GAIT to report and explain the detailed
learning process and the structured behaviors that the agent has
learned on each decision making. We report an experiment in which
the agent learned to successfully interact with its environment and to
avoid unfavorable interactions using regularities discovered through
interaction.
	%P 886 - 894