Contextual activation for agent-based simulation

When designing agent-based simulation, the choice of a coordination model is a key issue, since one of the difficulties is to link the activation of the agents with their context efficiently. Current solutions separate the activation phase from the action phase of the agents, and each action phase is based on local agent context analysis which is time-expensive. Moreover, because the link between the context and the action is an internal part of the agent, it is more difficult to modify the way the agent reacts to the context without altering the way the agent is implemented. Our proposal, called EASS (Environment as Active Support for Simulation), is a new approach for agent activation, where the context is analysed inside the environment and conditions the activation of the agents. The main result of contextual activation is to simplify the achievement of complex simulations and to decrease run-time. The EASS model has been implemented within the kernel of MadKit, a multi-agent platform, and the first results are given.