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RésuméCet article présente la modélisation d'une perturbation sur un réseau de transport. Le modèle proposé doit assurer la synthèse, l'évaluation et la mise à jour des informations disponibles afin de faciliter la tâche de l'opérateur assurant la surveillance du réseau. Pour atteindre cet objectif, nous proposons une modélisation formelle du concept de perturbation. Cette modélisation permet de capitaliser les connaissances disponibles au sein d'un poste de contrôle et d'assurer le suivi du processus en temps réel. Nous proposons également une représentation multi-agent d'un incident permettant l'intégration du traitement d'une perturbation au sein de l'activité d'un réseau de transport. Mots-clefs : Système d'aide à la décision, système multi-agent, transport. AbstractThis paper presents the modeling of a disturbance on a public transportation line. The proposed model allows the synthesis, evaluation and update of available information in order to help human regulators in their monitoring task. It begins with a formal modeling of the disturbance concept. This modeling makes it possible to capitalize the knowledge available within a monitoring station and to follow up the evolution of the disturbances in real time. The paper goes on to propose a multi-agent representation of an incident allowing the integration of the disturbance processing within the activity of a network system.

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Abstract-When designing agent-based simulation, the choice of a coordination model is a key issue, since one of the difficulties is to link the activation of the agents with their context efficiently. Current solutions separate the activation phase from the action phase of the agents, and each action phase is based on local agent context analysis which is time-expensive. Moreover, because the link between the context and the action is an internal part of the agent, it is more difficult to modify the way the agent reacts to the context without altering the way the agent is implemented. Our proposal, called EASS (Environment as Active Support for Simulation), is a new approach for agent activation, where the context is analysed inside the environment and conditions the activation of the agents. The main result of contextual activation is to simplify the achievement of complex simulations and to decrease run-time. The EASS model has been implemented within the kernel of MadKit, a multi-agent platform, and the first results are given.