Inverting the interaction cycle

Inverting the Interaction Cycle to Model Embodied Agents (2014). Olivier L. Georgeon and Amélie Cordier. Procedia Computer Science, 41, pp 243-248. 5th Annual International Conference on Biologically Inspired Cognitive Architectures.  doi:10.1016/j.procs.2014.11.109.

This paper relates very much to the first lesson of the IDEAL MOOC.

IDEAL MOOC

We are pleased to announce that our Developmental Artificial Intelligence MOOC is now open for registration.

Create your Ernest

Learn to create your own self-programming agent and robot in my course on developmental learning. French original version available here.

Lyon Startup Weekend

We won the jury's favorite prize ("coup de coeur du jury", 4^th prize) at the Lyon Startup Weekend ! The IDEAL team from left to right: Renaud Detaille, Raphaël Cazorla, Olivier Georgeon, Séverin Bruhat, and Julien Millet. Great thanks to the organizing team for this crazy unforgettable weekend. We love you too !

Watch the  finale video (our presentation from time 2:47:37 to 2:56:20, low audio quality French).

Inferring actions and observations from interactions

Garnier J., Georgeon O., and Cordier A., 2013. Inferring Actions and Observations from Interactions. In the Second Workshop on Goal Reasoning at Advances in Cognitive Systems (ACS2013), Baltimore, ML. 26-35.

Following the Radical Interactionism paradigm introduced previously, this paper investigates the construction of intentional actions and meaningful observations from regularities observed in sequences of sensorimotor interactions.

In doing so, we take the opposite stance of most machine learning approaches that seek to learn a mapping between a predefined space of actions and a predefined space of observations. Instead, our agent begins with a predefined space of interactions and learns actions and observations as secondary constructs. This paper also reports the Ernest 12 experiment.