Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Fundamentals of object-oriented simulation
Proceedings of the 30th conference on Winter simulation
Creating computer simulation systems: an introduction to the high level architecture
Creating computer simulation systems: an introduction to the high level architecture
Digital pheromone mechanisms for coordination of unmanned vehicles
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Parallel simulation: parallel and distributed simulation systems
Proceedings of the 33nd conference on Winter simulation
Theory of Modeling and Simulation
Theory of Modeling and Simulation
Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
A federated approach to distributed network simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
The web from a complex adaptive systems perspective
Journal of Computer and System Sciences
Give agents their artifacts: the A&A approach for engineering working environments in MAS
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 2010 ACM Symposium on Applied Computing
Objective coordination in multi-agent system engineering: design and implementation
Objective coordination in multi-agent system engineering: design and implementation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multi-level modeling as a society of interacting models
Proceedings of the Agent-Directed Simulation Symposium
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Complex systems simulations generally involve the interaction of different scientific fields. Human economies, ecosystems or dynamic computer networks such as P2P are good examples. Since models and simulators already exist in those fields, designing the simulation as a society of interacting and co-evolving models appears attractive. Beyond the technical issues to make different simulators cooperate, the challenges are to make the co-evolution design and implementation easier for the scientist that rarely know intricate modelling and simulation tools, and to facilitate the collaboration of different experts. Agents and artefacts (A&A) paradigm simplifies the design and the implementation of a society of interacting and co-evolving models. That is, the addition, the removal or the interchange of models require less effort. Contrary to classical approaches, we have built a decentralized co-evolution architecture based upon A&A and a data-driven coordination model. In this article, beyond the architecture presentation, we focus on the benefit provided by A&A used for multiple models co-evolution.