Buffet, OlivierDutech, AlainCharpillet, Francois2015-12-132015-12-130992-499Xhttp://hdl.handle.net/1885/85393The 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".Keywords: Decision theory; Learning systems; Markov processes; Markov Decision Problems; Multiple Motivations; Reinforcement Learning; Autonomous agents Markov Decision Problems; Multiple Motivations; Reinforcement Learning20052015-12-12