  Modern cyber-physical systems (CPS), such as our energy infrastructure, are
becoming increasingly complex: An ever-higher share of Artificial Intelligence
(AI)-based technologies use the Information and Communication Technology (ICT)
facet of energy systems for operation optimization, cost efficiency, and to
reach CO2 goals worldwide. At the same time, markets with increased flexibility
and ever shorter trade horizons enable the multi-stakeholder situation that is
emerging in this setting. These systems still form critical infrastructures
that need to perform with highest reliability. However, today's CPS are
becoming too complex to be analyzed in the traditional monolithic approach,
where each domain, e.g., power grid and ICT as well as the energy market, are
considered as separate entities while ignoring dependencies and side-effects.
To achieve an overall analysis, we introduce the concept for an application of
distributed artificial intelligence as a self-adaptive analysis tool that is
able to analyze the dependencies between domains in CPS by attacking them. It
eschews pre-configured domain knowledge, instead exploring the CPS domains for
emergent risk situations and exploitable loopholes in codices, with a focus on
rational market actors that exploit the system while still following the market
rules.
