Learning Collective Behaviors from Observation. (arXiv:2311.00875v1 [cs.LG])

We present a review of a series of learning methods used to identify the
structure of dynamical systems, aiming to understand emergent behaviors in
complex systems of interacting agents. These methods not only offer theoretical
guarantees of convergence but also demonstrate computational efficiency in
handling high-dimensional observational data. They can manage observation data
from both first- and second-order dynamical systems, accounting for
observation/stochastic noise, complex interaction rules, missing interaction
features, and real-world observations of interacting agent systems. The essence
of …

agents arxiv collective complex systems computational convergence data efficiency identify observation review series systems