Diagnosis (artificial intelligence)

As a subfield in artificial intelligence, diagnosis is concerned with the development of algorithms and techniques that are able to determine whether the behaviour of a system is correct.

The computation is based on observations, which provide information on the current behaviour.

This word comes from the medical context where a diagnosis is the process of identifying a disease by its symptoms.

The mechanic will first try to detect any abnormal behavior based on the observations on the car and his knowledge of this type of vehicle.

If he finds out that the behavior is abnormal, the mechanic will try to refine his diagnosis by using new observations and possibly testing the system, until he discovers the faulty component; the mechanic plays an important role in the vehicle diagnosis.

Using this experience, a mapping is built that efficiently associates the observations to the corresponding diagnoses.

An example is the computation of a diagnoser for the diagnosis of discrete event systems.

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 problem of diagnosability is very important when designing a system because on one hand one may want to reduce the number of sensors to reduce the cost, and on the other hand one may want to increase the number of sensors to increase the probability of detecting a faulty behavior.

One class of algorithms answers the question whether a system is diagnosable; another class looks for sets of sensors that make the system diagnosable, and optionally comply to criteria such as cost optimization.

In applications using model-based diagnosis, such a model is already present and doesn't need to be built from scratch.

DX is the annual International Workshop on Principles of Diagnosis that started in 1989.