The main problem is to provide a good description of such an examples base. (By analogy, we can say that the data base is the program, the learning tool is the compiler and the execution gives the knowledge). Basically such a learning system includes five main functions :

• PREPARE : to transform the example files from user format (ASCII, dBase, Excel, ...) into the own format of the system and to handle the discretization of the descriptors and the splitting of the initial data base into a training set and a test set.
  
• LEARN : to automatically extract a rules base from the training set according to the quality of available information (noise, sparseness of the training set,..).
  TEST : to experimentally evaluate the quality of the extracted rule on the test set.
  
• INCLEAR : to allow the expert to visualize IN CLEAR the extracted rules with the initial user format and to say what descriptors are kept.
  
• CONCLUDE : from the description of a new case, to deliver a conclusion based on the extracted rules.
  
  In all problems, it is necessary to consider one conclusion which may be a class or any continuous real number. Moreover, often, several conclusions may be considered together. The rules have to be automatically generated for each one of them.
  
  Then an optional but fundamental sixth function, OPTIMISE, based on genetic algorithms and other special optimization techniques, may be used to solve the inverse problem, i.e. when some conclusions and some descriptors have to belong to some given sets (or constraints), what are the possible solutions and, in some particular cases, what is the best solution if an objective function is given (cost, weight, efficiency ..)