Agent Architecture for modelling and simulation of multidynamical complex systems : a multibehaviors approach based on the “Agent MVC” pattern

Simulation Orientée Agent

Co-building and reuse of models are at the center of several studies in the field of simulation. However, in the more specific field of Multi-Agent Based Simulation (MABS), there is a lack of methodology to resolve these two issues, despite a strong need by experts.

Model co-building is essential to optimize knowledge sharing amongst different experts, but we often face divergent viewpoints. Existing methodologies for the MABS co-building allow only a low level of collaboration among experts during the initial phase of modeling, and between domain experts with modelers or computer scientists… In order to help this co-building, we propose and follow a methodology to facilitate this collaboration.

Model reuse can provide significant time savings, improve models’ quality and offer new knowledge. Some MABS methodologies in this area exist. However, in the spectrum of reuse, they are often limited to a full model’s reuse or agent’s reuse with the impossibility of reusing smaller parts such as behaviors.

The EDMMAS experiment was a concrete case of three successive model reuses. It allowed us to observe new complexity arising from the increase of agents’ behaviors. This creates a gap between operational model and conceptual model.

Our goal is to promote the reuse of models, agents and their behaviors.

To answer these questions, we propose in this thesis a new way to codify and integrate knowledge from different disciplines in the model, while using “composable” modules that facilitate reuse. We propose (i) a new agent architecture (aMVC), applied to a multidynamical approach (DOM), with the support (ii) of a methodology (MMC) based on the decomposition and reuse of behaviors.

Proposals (i) and (ii), allow us to lead a multidisciplinary MABS project with a large number of actors, helping the co-building of models through the introduction of synergies among the different actors involved in the modeling. They can work independently on their dynamics and the platform will integrate those, ensuring cohesion and robustness of the system. Our contributions include the ability to create the building blocks of the system independently, associate and combine them to form agents. This allows us to compare possibilities for the same dynamic and open the prospect of studying many alternate models of the same complex system, and then analyze at a very fine scale.