Section: New Results

Co-Simulation and multi-modeling

Participants : Laurent Ciarletta [contact] , Julien Siebert, Tom Leclerc.

Vincent Chevrier (MAIA team, LORIA) and Tomas Navarette are external collaborators.

Multiagent approach for multimodeling and simulation coupling.

Participants : Laurent Ciarletta [contact] , Julien Siebert.

Vincent Chevrier (MAIA team, LORIA) is an external collaborator.

this work has been extensively detailed in Julien Siebert's PhD thesis [3] and partially in Tom Leclerc's , with an application to ubiquitous adhoc networks and services.

This work has been done between the fields of ubiquitous networks and multi-agent based simulation. The main context is to study mutual influences existing between ubiquitous network performances and their users behaviours. We have highlighted the need for reusing and coupling modelling and simulation softwares together in order to simultaneously integrate several abstraction levels in the study. We target those needs by a multiagent approach and we propose a metamodel : AA4MM. The core idea in AA4MM is to build a society of models, simulators and simulation softwares that solves the core challenges of multimodelling and simulation coupling in an homogeneous perspective. AA4MM major contributions are the possibility to easily reuse, to make interoperable and modular existing heterogeneous models and softwares, to manage scale changes and a simulation algorithm fully decentralized. We apply this metamodel to the field of ubiquitous networks in order to target the question of mutual influences between networks performances and users behaviours.

Adaptive control of a complex system based on its multi-agent model

Participants : Laurent Ciarletta [contact] , Julien Siebert.

Vincent Chevrier (MAIA team, LORIA) and Tomas Navarette are external collaborators and main investigators of this theme.

As a starting point, we are exploring how the behavior and other factors such as spatial and temporal dimensions are mutually influencing and the impact of parameters variability of our models in environment where collective behaviors can emerge [6] . We did comparison of five different models. These models are built upon the same individual behavior hypothesis of a collective phenomenon present in peer-to-peer file exchange networks: "free-riding". We studied a global analytical model and four multi-agent models. Multi-agent models include the space and time dimensions rarely seen in the literature discussing aggregated models of the collective phenomenon in question. We have demonstrated that one individual decision algorithm can lead to contradictory information.

Using these results, we want to build a control mechanism for a complex/dynamic system. Specifically, we want to evaluate the effectiveness of creating a control mechanism based on a multi-agent model of the system.

Multi-agent models can be adapted to that purpose since usual approaches using analytical models as a basis can be intractable when dealing with such systems; and if we consider that the available control actions are meant to be applied locally, a multi-agent model is necessary. We are currently working on a case study within the dynamic networks domain, namely the free-riding phenomenon present in peer-to-peer networks.

We propose an architecture that gathers information from the system and uses it to parametrize and tune a set of multi-agent models. The outcome of simulations is used to decide which control actions have to be applied to the system, in order to achieve a predefined control objective. We consider that we do not have complete information to characterize the state of the system.