PAMELA: a generic and light multi-agent platform

Multi-agent frameworks are gaining popularity among the research community as they provide efficient and scalable tools for modelling distributed and/or social systems, enabling to simulate and investigate the behaviour of complex systems in a wide range of applications (such as road traffic, crowd evacuation, disease spreading, etc.). Existing MAS frameworks offer stable and widely used solutions for users that are already experienced with distributed systems simulation or the MAS paradigm. They are also often dedicated (or primarily designed) for a specific application. New users can see the learning curve associated with each framework as an obstacle, especially when they lack the theoretical knowledge about computer science or agents and they seek to build and run a very first proof-of-concept simulation. The work presented in this paper results from an effort towards providing more accessible MAS simulation tools, possibly further popularizing their use across different research fields. This paper introduces PAMELA: a novel generic collaborative open-source MAS framework that aims at being light, beginner-friendly, and that allows for fast prototyping through assisted scenario generation and powerful configuration. The tool can work with or without (for faster simulations) the integrated graphical user interface (designed for both testing and visualization). To make is more attractive to new programmers and to enable an easier interfacing with trending machine learning frameworks, PAMELA is entirely written in Python and only relies on standard libraries. We show the potential of PAMELA to quickly and easily provide running prototypes that could be used as proof-of-concept simulations before building more complex use cases in the same or a more specific MAS framework.