which enables the management of emergency situations by interpreting aspects communications created by the actors. Then, a preventive vigil system **(PVS) [1]**
was designed with the mean of some technologies used in the ICS modelling as: semantic features, ontologies, and agents with internal variables **and behavioural** automata. The PVS aims either to prevent a crisis or to deal with it with a main internal goal: detecting a crisis.

Since 2003, the architecture of the PVS was redesigned with a new specificity, that is the generic aspect; generic is used here with different meaning from [13]. A part of the global system, which is responsible of the dynamic information representation of the current situation, was applied to the game of Risk and tested thanks to a prototype implemented in Java [10]. However, we **postulate** that some parts of the architecture and, at a deeper level, some parts of the agents were independent of the subject used as application. Therefore, **the objective at** present is to connect this part to the other parts, that we present latter in this paper, and to test the whole system on various domains, as RoboCup Rescue [11] and e-learning.

We focus here on the modelling of the information representation part of the system that we intend to use it in a crisis management support system.

The paper begins with the presentation of the global system architecture.

The core of the system is constituted by a multiagent system (MAS) **which is** structured on three multiagent layers. Then, in section 3, we explain the way we formalise the environment state and we extract information related to it, which are written in the form of semantic features. The latter constitute data that feed the system permanently and that carry information about the current situation. The semantic features are handled by factual agents and are compared the one with the other using specific ontologies [2].

Factual agents, that compose the first layer of the core, are presented thereafter in section 4. Each agent carries a semantic feature and aims to reflect a partial part of the situation. We present their structures and their behaviours inside their organisation using internal automaton and indicators.

Finally, we present a short view about the game of Risk test in which we describe the model application and the behaviour of factual agents.

## 2 Architecture Of The Decision Support System

The role of the decision support system **(DSS) is to provide a decision-making**
support to the actors in order to assist them during a crisis case. The DSS allows also managers to anticipate the occur of potential incidents thanks to a dynamic and a continuous evaluation of the current situation. Evaluation is realised by comparing the current situation with past situations stored in a scenarios base. The latter can be viewed as one part of the knowledge we have on the **specific** domain.

The DSS is composed of a core and three parts which are connected **to it**
(figure 1):