Modulation of exploratory behavior for adaptation to the context
For autonomous agents (children, animals or robots), exploratory learning is essential as it allows them to take advantage of their past experiences in order to improve their reactions in any situation similar to a situation already experimented. We have already exposed in Blanchard and Canamero (2005) how a robot can learn which situations it should memorize and try to reach, but we expose here architectures allowing the robot to take initiatives and explore new situations by itself. However, exploring is a risky behavior and we propose to moderate this behavior using novelty and context based on observations of animals behaviors. After having implemented and tested these architectures, we present a very interesting emergent behavior which is low-level imitation modulated by context.