EcoSFERES: A Tool for the Design of Self-Organized Agent-Based Applications

In this article we present EcoSFERES, a framework that helps applying learning and evolutionary algorithms to the design of multi-agent systems (MAS). This framework includes tightly linked abstractions for evolutionary computing and multi-agent based simulations, and additionally, agent learning policies can also be included and reused straightforwardly. In particular, EcoSFERES makes it possible to implement a variety of learning and evolutionary techniques in order to compare their results in identical conditions, and it also facilitates important changes in the simulation setup. The main added values of this article are, on the one hand, to introduce the first development framework that provides a generic way of using evolutionary techniques for the design of MAS, and, on the other hand, to show that the tuning of MAS can be facilitated by a well designed development framework.