Life and self-organization on the way to artificial intelligence for collective dynamics

This work is dedicated to the study, modeling, and simulation, of the collective dynamics of interacting living entities. The first perspective is to develop a mathematical theory of swarm intelligence for the above mentioned systems. The second perspective is to design the conceptual tools for a theory of artificial intelligence. The aim is to model a dynamics where interacting entities learn from other entities as well as from the environment and external actions. Then, out of this collective learning process, each entity develops a strategy to pursue specific goals through a decision making process that leads to the dynamic. The approach is based on developments of the kinetic theory of active particles. This paper does not naively states that the problem of artificial intelligence for collective dynamics has been exhaustively considered, but some hints are proposed to contribute to such a challenging perspective in view of further developments.