Tom Lenaerts (ULB) - "Cooperative AI, game theoretical research into socially beneficial AI"
The advent of complex high-quality autonomous AI systems raises a number of questions on how these systems should decide and act when released into the wild without (or with minimal) human supervision. To avoid disasters, either exogenous (regulations) or endogenous (design) solutions are being proposed. Essentially both solutions want to ensure that the use of AI in business and society is able to align both individual and social preferences and norms, without causing a negative disruption.
In this seminar, I will show that evolutionary game theory, a theoretical framework for studying multi-agent interactions and learning, and related behavioural experiments can help achieve this ambition. Central to this ambition is our ongoing work on studying mechanisms that may influence a collective of agents to prefer cooperation in the context of competitive situations, where individual and collective preferences are not be aligned. I will start this seminar with a short introduction to evolutionary game theory and then zoom in on some cases where we will use both simulations and human experiments to understand how cooperation or coordination can be improved.
What sets EGT apart from other learning paradigms to explore this cooperative AI question is that it immediately combines both individual and societal effects in one framework. It is this duality that needs to be incorporated into the study of cooperative AI systems in order to achieve intelligent systems that are aligned with both an individual’s preferences and norms as well as those of society as a whole.