Allowing intelligent agents to deal with unforeseen situations that have not been considered during development in a smart way is a first step for increasing their autonomy. This requires diagnostic capabilities to detect the unforeseen situation and to identify a root cause that can be used afterward for carrying out repair and other compensating actions. Herein, foundations for diagnostic reasoning based on models of the system are provided. In particular, a diagnostic solution is presented that utilizes answer set solvers, which allow implementing non‐monotonic reasoning. The underlying ideas are introduced, an algorithm is discussed, and experimental results are obtained to clarify the question whether the approach can be used in practical applications. The obtained results indicate that answer set solving provides similar and sometimes even better results than specialized diagnosis algorithms, and can be used in practice.
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