Développement autonome des comportements de base d'un agent
Olivier Buffet
Revue d'Intelligence Artificielle (RIA) Volume 19, Number 4-5, pp. 603-632, 2005. ISSN 0992-499X

Abstract

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 .

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