Forum Numerica - Tristan Cazenave: "Monte Carlo Search"

Monte Carlo Search is a family of general search algorithms that have many applications in different domains. It is the state of the art in perfect and imperfect information games. Other applications include the RNA inverse folding problem, Logistics, Multiple Sequence Alignment, General Game Playing, Puzzles, 3D Packing with Object Orientation, Cooperative Pathfinding, Software testing and heuristic Model-Checking. In recent years, many researchers have explored different variants of the algorithms, their relations to Deep Reinforcement Learning and their different applications. The talk will give a broad overview of Monte Carlo Search and of its applications.

Tristan Cazenave is professor of Artificial Intelligence at LAMSADE, University Paris Dauphine-PSL and CNRS. He works on Monte Carlo Search and Deep Reinforcement Learning for games and optimization. He defended a PhD thesis on Machine Learning for computer Go in 1996 at Sorbonne University.