Optimizing Context Computation for Multiagent Simulations

The execution of a multiagent-based simulation (MABS) model necessitates a scheduler that synchronizes the agents execution and simulates the simultaneity of their behaviors. In the majority of MABS frameworks the scheduler activates the agents who compute their context to decide the action to execute. This context computation process is time-consuming and is one of the barriers to increased use of MABS for large simulations. The context of an agent is computed based on all information he possesses about himself, the other agents and the objects of the environment that are accessible to him. One of the issues is to find this information and to identify the information subsets that make sense for the agent. In the majority of MABS, the context computation is hidden in the agent processes and there is no specific algorithm enabling to decrease its computing. Our proposal is the modeling of this subset of information identifying a context by a so called "filter" and an algorithm to find efficiently for each agent all their filters. The accessible information are given by the scheduler to the agents who browse a tree of filters to select the right information. With these filters, the agents know their context and decide which action to execute. Our algorithm is compared to a classical context identification. Promising results are presented and discussed.