  Risk management resulting from the actions and states of the different
elements making up a operating room is a major concern during a surgical
procedure. Agent-based simulation shows an interest through its interaction
concepts, interactivity and autonomy of different simulator entities. We want
in our study to implement a generator of alerts to listen the evolution of
different settings applied to the simulator of agents (human fatigue, material
efficiency, infection rate ...). This article presents our model, its
implementation and the first results obtained. It should be noted that this
study also made it possible to identify several scientific obstacles, such as
the integration of different levels of abstraction, the coupling of species,
the coexistence of several scales in the same environment and the deduction of
unpredictable alerts. Case-based reasoning (CBR) is a beginning of response
relative to the last lock mentioned and will be discussed in this paper.

Artificial Intelligence;Multiagent Systems