Systems for interpretation and diagnosis
This system interpreted geological data and made recommendations of suitable sites for mineral prospecting. The system made use of Bayesian updating as a means of handling uncertainty (see Chapter 3).
This system interpreted mass-spectrometry data, and was notable for its use of a three-phase approach to the problem:
A large number of intelligent systems has been produced more recently, using many different techniques, to tackle a range of diagnosis and interpretation problems in science, technology, and engineering. Rule-based diagnostic systems have been applied to power plants , electronic circuits , furnaces , an oxygen generator for use on Mars , and batteries in the Hubble space telescope . Bayesian updating and fuzzy logic have been used in nuclear power generation  and automobile assembly , respectively. Neural networks have been used for pump diagnosis , and a neural network–rule-based system hybrid has been applied to the diagnosis of electrical discharge machines . One of the most important techniques for diagnosis is case-based reasoning (CBR), described in Chapter 6. CBR has been used to diagnose faults in electronic circuits , emergency battery backup systems , and software . We will also see in this chapter the importance of models of physical systems such as power plants . Applications of intelligent systems for the more general problem of interpretation include the interpretation of drawings , seismic data , optical spectra , and ultrasonic images . The last is a hybrid system, described in a detailed case study in Section 11.5.
11.2 Deduction and abduction for diagnosis