Multi-agent systems, Knowledge representation and reasoning
lang [at] lamsade.dauphine.fr
CNRS senior scientist (2008-), LAMSADE (CNRS, PSL, Université Paris-Dauphine). CNRS silver medal 2017. EurAI fellow since 2009. Associate editor of Journal of Artificial Intelligence Research (2009-2015), Artificial Intelligence Journal (2010-2016), Social Choice and Welfare (2016-). Program chair of IJCAIECAI-2018, General chair of ECAI-2020.
Topics of interest
Computational social choice, algorithmic game theory, fairness, algorithmic decision theory, knowledge representation
Project in Prairie
Jérôme Lang’s research project focuses on the use of social-choice theoretic, decision-theoretic, and game-theoretic principles and tools for AI. This goes in two opposite directions: from economics to AI, and from AI to economics. He will especially focus in domains where AI helps taking decisions for groups of people. He plans to work on social-choice principles for ethics and fairness in AI, and more generally for assessing the social acceptability of AI algorithms.
Normative criteria developed in economics (such as fairness, equity, anonymity, privacy, strategyproofness, efficiency), can be help defining frameworks for analysing the ethics and social acceptability of AI algorithms, and investigating the trade-offs that have to be made between incompatible criteria. Notions of fairness in AI and social choice are slightly different but convergent, and the various notions of fairness studied in social choice are relevant to AI research. Moreover, AI techniques are useful for making collective decisions: especially, ML methods for learning and eliciting users’ preferences help making collective decisions that offer a good trade-off between the quality of the outcome and the communication burden. This applies to various areas of public decision making, such as allocation problems (matching students to universities, organs to patients, designing fair and robust schedules in hospitals or high schools), or fair and efficient public spending.
tristan.cazenave [at] dauphine.psl.eu
Professor, Computer Science, LAMSADE, University Paris-Dauphine, PSL (Sep. 2008-). Editor in Chief: International Computer Games Association Journal (2017-).
Topics of interest
Artificial Intelligence, Monte Carlo Search, Deep Learning, Games, Optimization
Project in Prairie
Tristan Cazenave will address Monte Carlo Search and Deep Reinforcement Learning for games and optimization problems. He will act as the director of the IASD research master in Artificial Intelligence of PSL.
Artificial intelligence is not well appreciated by medical doctors who fear being replaced by computers. By contrast, I believe that AI represents a unique chance for caregivers to assist them to better detect and care patients. For practitioners to take on this opportunity, we need to develop more robust and interpretable methods than the current state-of-the-art. This is what I will do within PRAIRIE by fostering multidisciplinary collaborations with Paris hospitals. I will also develop innovative training programs within the medical school to bridge the gap between computer scientists and physicians.
My current research topics are:
• Tailor the Monte Carlo search to the problem being learned. For example for single player games a Nested Monte Carlo search might learn better than the standard PUCT search. Similarly for incomplete information problems PUCT might not be the best algorithm.
• Accelerate Alpha Zero type Deep Reinforcement Learning. The current algorithm requires huge computations. Finding ways to learn faster is important.
• Apply a combination of Monte Carlo search and Deep Learning to various
• Explore various network architectures. For example residual networks enable to train deeper net-works faster and with better results. Layers such as Average Spatial Pooling improve much the quality of value networks. New network architectures may improve substantially the level of play.