Diagnosis is the reasoning process that aims at determining whether certain properties (such as the occurrence of a failure, of an unexpected measurement, or of a deviation from prescribed behavior) hold at a given time inside a system (fault diagnosis) and/or in its environment (situation diagnosis) and identifying their causes. Diagnosis reasoning is thus crucial for many reasons: safety/maintainability in supervised systems, robustness/decision autonomy of smart agents in their partially observed environment, reconfiguration of business strategies after a failure... Despite the huge spectrum of applications, Model-Based Diagnosis (MBD) problems
are generic as any diagnosis algorithm relies on a class of models, or a modeling framework, that represents a larger class of systems.

The purpose of this workshop is to gather researchers from several related fields such as Model-Based Diagnosis (DX community), knowledge-compilation, satisfiability (SAT), constraint satisfaction and optimization (CSP) to exchange ideas about  the following questions:

• What are the limits of MBD algorithms?
• How to measure the performance of MBD algorithms? benchmarks?
• What are the links between the model properties (diagnosability, detectability,...) and MBD performance?
• Which AI reasoning techniques to analyse the models and their properties?