SUPERVISING THE HEURISTIC LEARNING IN A DIAGNOSTIC EXPERT SYSTEM
The paper presents the organization of a diagnostic expert system with some capability of heuristic learning. In particular the role of the supervisor is analyzed and a set of strategies is defined which allows the system to implement different policies (conservative and non-conservative approaches). The organization of the system has been strongly influenced by the results obtained so far in investigating the properties of neural nets and of human learning. The learning system is able to revise the knowledge bases used by the consultation system by taking into account the experience gained in solving cases as well as the confirmation (disconfirmation) of diagnoses provided by the external world. In particular the learning system revises the membership functions between findings and diagnostic hypotheses and the membership relations defined among diagnostic hypotheses. The paper focusses its attention on the description of the behavior of the learning system in the conservative approach and discusses some alternative solutions for memory organization.
heuristic learning expert systems membership functions artificial intelligence diagnosis prototypical knowledge approximate reasoning