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. If the system is not functionning correctly, the algorithm should be determine, as accurately as possible, which part of the system is failing, and which kind of fault it is facing. The computation is based on observations, which provide informations on the current behaviour. Hondas intelligent humanoid robot AI redirects here. ...

The expression diagnosis also refers to the answer of the question of whether the system is malfunctioning or not, and to the process of computing the answer. This word comes from the medical context where a diagnosis is the process of identifying a disease by its symptoms. Diagnosis (from the Greek words dia = by and gnosis = knowledge) is the process of identifying a disease by its signs, symptoms and results of various diagnostic procedures. ...

Example

An example of diagnosis is the process of a garage mechanic with an automobile. The mechanic will first try to detect any abnormal behaviour based on the observations on the car and his knowledge of this type of vehicle. If he finds out that the behaviour is abnormal, the mechanic will try to refinement his diagnosis by using new observations and possibly testing the system, until he discovers the abnormal component.

Expert diagnosis

The expert diagnosis (or diagnosis by expert system) is based on the experience with the system. Using this experience, a mapping is built that efficiently associates the observations to the corresponding diagnoses. An expert system is a class of computer programs developed by researchers in artificial intelligence during the 1970s and applied commercially throughout the 1980s. ...

The experience can be provided:

• By a human operator. In this case, the human knowledge must be translated into a computer language.
• By examples of the system behaviour. In this case, the examples must be classified as correct or faulty (and, in the latter case, by the type of fault). Machine learning methods are then used to generalize from the examples.

The main drawbacks of these methods are: As a broad subfield of artificial intelligence, Machine learning is concerned with the development of algorithms and techniques, which allow computers to learn. At a general level, there are two types of learning: inductive, and deductive. ...

• The difficulty acquirring the expertise. The expertise is typically only available after a long period of use of the system (or similar systems). Thus, these methods are unsuitable for safety- or mission-critical systems (such as a nuclear power plant, or a robot operating in space). Moreover, the acquired expert knowledge can never be guaranteed to be complete. In case a previously unseen behaviour occurs, leading to a unexpected observation, it is impossible to give a diagnosis.
• The complexity of the learning. The off-line process of building an expert system can require a large amount of time and computer memory.
• The size of the final expert system. As the expert system aims to map any observation to a diagnosis, it will in some cases require a huge amount of storage space.
• The lack of robustness. If even a small modification is made on the system, the process of constructing the expert system must be repeated.

A slightly different approach is to build an expert system from a model of the system rather than directly from an expertise. An example is the computation of a diagnoser for the diagnosis of discrete event systems. This approach can be seen as model-based, but it benefit from some advantages and suffers some drawbacks of the expert system approach. For the Computer Science term, see Computational complexity theory. ... It has been suggested that this article or section be merged into Robustness. ...

Model-based diagnosis

Model-based diagnosis is an example of abductive reasoning using a model of the system. In general, it works as follows: Abduction, or abductive reasoning, is the process of reasoning to the best explanations. ... An abstract model (or conceptual model) is a theoretical construct that represents physical, biological or social processes, with a set of variables and a set of logical and quantitative relationships between them. ...

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. Abduction, or abductive reasoning, is the process of reasoning to the best explanations. ...

Diagnosability

A system is said to be diagnosable if whatever the behaviour of the system, we will be able to determine without ambiguity a unique diagnosis.

The problem of diagnosability is very important when designing a system because on the 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 behaviour. Algorithms that determine the diagnosability of a system can be used to answer this problem.

The diagnosability of a system is generally computed from the model of the system. Thus, it can only be checked in the case of model-based diagnosis.

Bibliography

Readings in model-based diagnosis, W. Hamscher, L. Console et J. de Kleer, Morgan Kaufmann Publishers Inc., 1992.

External links

DX workshops

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

• DX 2006
• DX 2005
• DX 2004
• DX 2003
• DX 2002
• DX 2001
• DX 2000
• DX 1999
• DX 1998