Abstract: "This dissertation reports on the development of a model and system for medical diagnosis based on the use of general purpose reasoning methods and a knowledge base which can be built almost entirely from existing medical texts. The resulting system is evaluated empirically by running 63 patient protocols collected from a community health centre on the system, and comparing the diagnoses with those given by medical experts. It is often the case in Artificial Intelligence that general purpose reasoning methods (such as default reasoning, classification, planning, inductive learning) are developed at a theoretical level but are not used in real applications. One possible reason for this is that real applications typically need several reasoning strategies to solve a problem. Combining reasoning strategies, each of which uses a different representation of knowledge is non-trivial. This thesis addresses the issue of combining strategies in a real application. Each of the strategies used required some modification, either as a result of the representation chosen, or as a result of the application demands. These modifications can indicate fruitful directions for future research. One well known problem in building A.I. systems is the building of the knowledge base. This study examines the use of a representation and method which allowed for the knowledge base to be built from standard medical texts with only minimal input from a medical expert. The evaluation of the resulting system indicated that in cases where medical experts were in agreement, the system almost always reached the same diagnosis. In cases where medical doctors themselves disagreed the system behaved within the range of the medical doctors in the study."