The multi-agent approach to artificial general intelligence
Human inspired AI aims at constructing agents that are capable of completing complex tasks in diverse environments, and exhibit human-like cognitive flexibility. However, the human capacity for learning is not nearly as adaptive as we usually assume: individual humans cannot even figure out how to survive in our own ancestral ecological niche (hunting and gathering). We owe our success to our uniquely developed ability to learn from others. In this talk I will argue that this observation can be leveraged to advance artificial intelligence research through multi-agent reinforcement learning (MARL). I will outline the main advantages and key challenges of this approach, and I will survey recent results from the MARL research group at DeepMind. The technical part of my talk will focus on multi-agent learning dynamics, social dilemmas, emergent coordination and social influence.
Andrea Tacchetti is a Research Scientist at DeepMind in London, UK. His research spans across Multi-agent Reinforcement Learning, Social Perception and Relational Reasoning. Before joining DeepMind he obtained his PhD from MIT under the supervision of Prof. Tomaso Poggio. In 2018 Dr. Tacchetti received the APS-select award for distinction in scholarship from The American Physiological Society.
2019-02-18 at 3:00 pm (subject to variability)