Diagnosis applied CSP based on models

In the last decades, model-based diagnosis has been an active research topic for the Artificial Intelligence community. It uses the explicit model of a system, the system inputs and the measured system outputs, in order to identify the subsystems that can generate faults. The system or the process that incorporates diagnosis may reduce costs and provide more security. Some models used in engineering are based on constraint logic programming (CLP) in order to obtain the system diagnosis. In this paper we propose a methodology for the system diagnosis as a constraint satisfaction problem (CSP). Using this methodology it is possible to incorporate, the advances and optimizations achieved for the search of solutions in CSP. This methodology also offers the possibility of applying diagnosis to other areas, such as software diagnosis. Software diagnosis allows the identification of the program bugs. A bug occurs when there is not matching between the specified results and the observed results after a program execution.

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