Multidimensional environment to contextualize agent interactions: Application to the simulation of urban road traffic

Multidimensional environment to contextualize agent interactions: Application to the simulation of urban road traffic

Stéphane Galland Flavien Balbo Gauthier Picard Olivier Boissier Nicolas Gaud Sebastian Rodriguez

Université Bourgogne Franche-Comté, UTBM, LE2I UMR CNRS 6306 13 rue Ernest Thierry-Mieg 90010 Belfort, France

Laboratoire Hubert Curien UMR CNRS 5516, Institut Henri Fayol MINES Saint-Etienne 158 Cours Fauriel 42000 Saint-Étienne, France

Grupo de Investigación en Tecnologías Informáticas Avanzadas Facultad Regional Tucumán, Universidad Tecnológica Nacional Rivadavia 1050, San Miguel de Tucumán, CPA T4001JJD, Agentine

Corresponding Author Email: 
30 April 2016
| Citation

The environment, as a space shared between agents is essential to multi-agents systems (MAS). Depending on the systems, it responds to dierent points of view. It is described as physical or communicative as that agents interact through situated actions or exchanging messages, or social if a social model governs the interactions. Each of these views could be declined into a dimension with its own model. For a complex system in which dierent dimensions should be combined, there are only ad-hoc solutions for a global environment model. The consequence is a limited reusability and modularity of the use of environment models. An alternative is to consider the environment as the juxtaposition of its dimensions and make the agent the place for combining the information conveyed in these dimensions. This choice of design increases the agent complexity, and makes them dependent upon the mechanisms for managing the interactions between the dimensions. Finally, the implementation of contextual interactions, i.e. constrained by the rules of the MAS, requires a management of the interactions that is independent of the agents. In this paper, a model is proposed for ensuring the combination of the environment dimensions for the modeling and implementation of contextualized interactions between agents. This model is developed with the SARL agent-oriented programming language. This proposal is illustrated with a road trac simulation application to the city of Belfort.


environment as interaction support, physic environment, communication environment, programming language, road traffic.

1. Introduction
2. État de l’art
3. Positionnement
4. Modèle de l’environnement multidimensionnel
5. Implantation en SARL
6. Modélisation du cas d’application de trafic routier
7. Conclusion et perspectives L’environnement

Béhé F., Galland S., Gaud N., Nicolle C., Koukam A. (2014, janvier). An ontology-based metamodel for multiagent-based simulations. International Journal on Simulation Modelling, Practice, and Theory, vol. 40, p. 64-85. Consulté sur S1569190X13001342

Bhouri N., Balbo F., Pinson S. (2012). An agent-based computational approach for urban traffic regulation. Progress in AI, vol. 1, no 2, p. 139-147.

Claes R., Holvoet T., Weyns D. (2011). A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. Intelligent Transportation Systems, IEEE Transactions on, vol. 12, no 2, p. 364–373.

Farenc N., Boulic R., Thalmann D. (1999). An informed environment dedicated to the simulationof virtual humans in urban context. In Proceedings of eurographics 99, p. 309-318.

Galland S., Gaud N. (2015, octobre). Organizational and holonic modelling of a simulated andsynthetic spatial environment. E4MAS 2014 - 10 years later, LNAI, vol. 9068, no 1, p. 1-23.Consulté sur

Gechter F., Contet J.-M., Lamotte O., Galland S., Koukam A. (2012, mai). Virtual intelligent vehicle urban simulator: Application to vehicle platoon evaluation. Simulation Modelling Practice and Theory (SIMPAT), vol. 24, p. 103-114.

Gouaïch A., Michel F. (2005, may). Towards a unified view of the environment(s) within multi-agent systems. Informatica, vol. 29, no 4, p. 423-432.

Grignard A., Taillandier P., Gaudou B., Vo D. A., Huynh N. Q., Drogoul A. (2013). GAMA 1.6: Advancing the art of complex agent-based modeling and simulation. In 16th international conference on principles and practices in multi-agent systems (prima), vol. 8291, p. 242-258.

Hall E. T. (1990). The hidden dimension (Reissue éd.). New York, Anchor.

Mathieu P., Picault S., Secq Y. (2014). Les environnements: en avoir ou pas? formalisation du concept et patterns d’implémentation. In Jfsma, p. 55–64.

Michel F. (2007, mai). The IRM4S model: the influence/reaction principle for multiagent based simulation. In Sixth international joint conference on autonomous agents and multiagent systems (aamas07). ACM.

Musse S., Thalmann D. (2001). A hierarchical model for real time simulation of virtual human crowds. IEEE Transactions on Visualization and Computer Graphics, vol. 7, no 2, p. 152-164.

Odell J., Parunak H., Fleisher M., Brueckner S. (2009). Modeling Agents and their Environment. In Agent-oriented software engineering III, vol. 2585. N.Y. (USA), Springer-Verlag.

Pe¯na J., Levy R., Hinchey M., Ruiz-Cortés A. (2012). Dealing with complexity in agentoriented software engineering: The importance of interactions. In Conquering complexity, p. 191-214. Springer London.

Piunti M., Ricci A., Boissier O., Hübner J. (2009). Embodying organisations in multi-agent work environments. In Ieee/wic/acm Int. conf. on web intelligence and intelligent agent technology (wi-iat 2009). Milan, Italy..

Platon E., Sabouret N., Honiden S. (2007). Tag interactions in multiagent systems: Environment support. In Environment for multi-agent systems (e4mas), vol. 4389, p. 106-123. Springer.

Ricci A., Piunti M., Viroli M. (2011). Environment programming in multi-agent systems: an artifact-based perspective. Autonomous Agents and Multi-Agent Systems, vol. 23, no 2,p. 158–192.

Ricci A., Viroli M., Omicini A. (2005, July). Programming MAS with artifacts. In International workshop on programming multi-agent systems. Springer Verlag.

Rodriguez S., Gaud N., Galland S. (2014, Aug). SARL: A general-purpose agent-oriented programming language. In Web intelligence (wi) and intelligent agent technologies (iat), 2014 ieee/wic/acm international joint conferences on, vol. 3, p. 103-110.

Rodriguez S., Hilaire V., Gaud N., Galland S., Koukam A. (2011, mars). Holonic multi-agent systems. In Self-organizing software: From natural to artificial adaptation, first éd., p. 238-263. Springer.

Saunier J., Balbo F., Pinson S. (2014). A formal model of communication and context awareness in multiagent systems. Journal of Logic, Language and Information, p. 1-29. Consulté sur

Saunier J., Carrascosa C., Galland S., Kanmeugne P. s. (2015, octobre). Agent bodies: An interface between agent and environment. E4MAS 2014 - 10 years later, LNAI, vol. 9068, no 1, p. 1-16. Consulté sur

Thalmann D., Musse S. R. (2007). Crowd simulation. Springer.

Weyns D., Omicini A., Odell J. (2007, février). Environment as a first-class abstraction in multi-agent systems. Autonomous Agents and Multi-Agent Systems, vol. 14, no 1, p. 5-30. (Special Issue on Environments for Multi-agent Systems)

Weyns D., Steegmans E., Holvoet T. (2004). Protocol based communication for situated multiagent systems. In Proceedings of the third international joint conference on autonomous agents and multiagent systems-volume 1, p. 118–125.

Wilensky U., Rand B. (2015). Introduction to agent-based modeling: Modeling natural, social and engineered complex systems with NetLogo. The MIT Press.

Zargayouna M., Balbo F. (2013). Langage de coordination multi-agent sécurisé. Revue d’Intelligence Artificielle, vol. 27, no 3, p. 271-298.