<div>The intrinsic complexity of self-organising multi-agent systems calls for the use of formal methods to predict global system evolutions at early stages of the design process.
In particular, we evaluate the use of simulations of high-level system models to analyse properties of a design, which can anticipate the detection of wrong design choices and the tuning of system parameters, so as to rapidly converge to given overall requirements and performance factors.</div>

<div>We take abnormal behaviour detection as a case, and devise an architecture inspired by principles from human immune systems.
This is based on the TuCSoN infrastructure, which provides agents with an environment of artefacts&mdash;most notably coordination artefacts and agent coordination contexts.
We then use stochastic &pi;-calculus for specifying and running quantitative, large-scale simulations, which allow us to verify the basic applicability of our ID and obtain a preliminary set of its main working parameters.</div>