Abstract (EN)

Agent-based simulation is used to understand complex systems and to experiment several scenarios. In classical simulation frameworks, a pitfall is the fact that the action phase, based on local agent context analysis, is repeated in each agent at each time cycle during the simulation execution. This analysis inside the agents reduces agent flexibility and limits agent behavior reuse in various simulations. If the designer wants to modify the way the agent reacts to the context, he could not do it without altering the way the agent is implemented because the link between agent context and agent actions is an internal part of the agent. Our proposition, called EASS, is a new agent-based simulation framework, where the context is analyzed by the environment and where agent activation is based on context evaluation. This activation process is called contextual activation. The main advantage of contextual activation is the improvement of complex agent simulation design in terms of flexibility and run-time.