This paper presents a multi-level network-based approach to study complex systems formed by multiple autonomous agents. The fundamental idea behind this approach is that the agents of a system (represented by network vertices) and their interactions (represented by edges) can be assembled to form structures. Structures are considered to be at one hierarchical level above the agents and interactions that form them, leading to a multi-level organisation.

Analysing complex systems represented by multi-level networks make possible the study of the relationships between network topology and dynamics to the system's global outcome.

The framework proposed in this paper is exemplified using data from the RoboCup Football Simulation League, in which simulated autonomous robotic agents play games of football in a 2D environment.