Incremental Development of Diagnostic Set-Covering Models with Therapy Effects

We will consider set–covering models for diagnostic tasks handling uncertain knowledge. Because these models have to be build manually by domain experts, we will show how this effort can be reduced by an incremental development of the set–covering models. Thus simple models can be enhanced by similarities, weights and uncertainty to increase the quality of the knowledge and the resulting system. We will also present mechanisms for generating set–covering models when higher level knowledge about causation effects is available. Finally, we will motivate how our approach can be used for implementing diagnosis systems including therapy effects.