Diagnosis (artificial Intelligence) - Model-based Diagnosis

Model-based Diagnosis

Model-based diagnosis is an example of abductive reasoning using a model of the system. In general, it works as follows:

We have a model that describes the behaviour of the system (or artefact). The model is an abstraction of the behaviour of the system and can be incomplete. In particular, the faulty behaviour is generally little-known, and the faulty model may thus not be represented. Given observations of the system, the diagnosis system simulates the system using the model, and compares the observations actually made to the observations predicted by the simulation.

The modelling can be simplified by the following rules (where is the Abnormal predicate):

(fault model)

The semantics of these formulae is the following: if the behaviour of the system is not abnormal (i.e. if it is normal), then the internal (unobservable) behaviour will be and the observable behaviour . Otherwise, the internal behaviour will be and the observable behaviour . Given the observations, the problem is to determine whether the system behaviour is normal or not ( or ). This is an example of abductive reasoning.