Machine Learning through analogies formalized with tools from Category Theory
Industrial applications relying on Machine Learning (ML) to control robotic systems need to learn with few trials. The number of experiments in this context is limited by either the available time or budget. Such a goal can be attained by using knowledge transfer through analogies, both of which can be mathematically formalized with tools from Category Theory. This leads to a new ML approach that transposes accumulated knowledge to new configurations, while still relying on existing ML tools. Illustrations of this innovative solution are presented on a cyber-physical system, a slot-car game. This method also proves to be versatile enough to be used on simulated systems as well, as it is demonstrated on the Atari 2600 games.
Lionel Cordesses holds a Ph.D. in Computer Vision and Control. After developing autonomous vehicles in the late 90s, he joined the Renault group in France where he created and led the Engine Control team. Then, after six years as a Project Manager in the field of Electric Vehicles, he crossed the Atlantic to build and lead Renault’s Artificial Intelligence team in Sunnyvale, CA. Lionel is also an Independent Expert in Signal Processing. He loves to hear "we know it cannot be done", as this presents an exciting challenge to find a unique solution and ultimately create new patents (34 and counting)