Learning to solve real-world puzzles: from Sudoku to protein design

Séminaire à 13h30.

Résumé : Real-life decision making often involves reasoning on ill-defined problems, where exact constraints or parameters (such as costs) are unknown. The goal of neuro-symbolic (NeSy) AI is to automatize the definition of problem parameters by using Deep Learning to extract knowledge out of the environment. The main challenge here is to combine discrete optimization for reasoning with continuous optimization for learning. I will present one method to learn discrete graphical models, the reasoning framework we chose. We first assessed it on learning the rules of Sudoku, a popular benchmark for NeSy methods. We then apply it to Computational Protein Design, a real-world problem that can be described and tackled with graphical models. We deep learned the interactions within existing proteins to better guide the design towards new proteins of interest.