Developpement autonome des comportements de base dun agent

The problem addressed in this article is that of automatically designing autonomous agents having to solve complex tasks involving several -and possibly concurrent- objectives. We propose a modular approach based on the principles of action selection where the actions recommanded by several basic behaviors are combined in a global decision. In this framework, our main contribution is a method making an agent able to automatically define and build the basic behaviors it needs through incremental reinforcement learning methods. This way, we obtain a very autonomous architecture requiring very few hand-coding. This approach is tested and discussed on a representative problem taken from the "tile-world".