Modelling autonomous virtual agents and behaviours in a virtual environment for risk.

International audienceOur research deals with the design of a training system to support decision-making in the preparation and the management of maintenance interventions in high-risk industries namely SEVESO sites. The proposed system incorporates virtual reality and artificial intelligence to simulate virtual autonomous characters and their cognitive processes in dangerous working situations. It generates behaviour-based errors to support learning and risk prevention. It uses new mechanisms taking into account human factors with respect to cognitive modelling of human behaviour regarding risky situations. In the simulated environment the trainee can visualize the risks incurred during his work with the virtual agents. The emergent risks depend on the cognitive characteristics of the virtual operators and on the expertise of the trainee. We propose a multi-agent system to support the control of virtual operators represented by virtual cognitive agents. The difference with a classic MAS is that our cognitive agents are enriched with a planner for selecting actions according to goals, the environment and to the personal characteristics of the agents (time pressure, caution, tiredness, hunger)