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Modern cyber-physical systems (CPS), such as our energy infrastructure, arebecoming 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 toreach CO2 goals worldwide. At the same time, markets with increased flexibilityand ever shorter trade horizons enable the multi-stakeholder situation that isemerging in this setting. These systems still form critical infrastructuresthat need to perform with highest reliability. However, today's CPS arebecoming 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, areconsidered as separate entities while ignoring dependencies and side-effects.To achieve an overall analysis, we introduce the concept for an application ofdistributed artificial intelligence as a self-adaptive analysis tool that isable to analyze the dependencies between domains in CPS by attacking them. Iteschews pre-configured domain knowledge, instead exploring the CPS domains foremergent risk situations and exploitable loopholes in codices, with a focus onrational market actors that exploit the system while still following the marketrules.

Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence