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The major AI problems that arise in designing a consultation program involve choices of knowledge representations, diagnostic interpretation strategies, and treatment planning strategies. The need to justify decisions and update the knowledge base in the light of new research findings places a premium on the modularity of a representation and the ease with which its reasoning procedures can be explained. In both diagnosis and treatment decisions, the relative advantages and disadvantages of different schemes for quantifying the uncertainty of inferences raises difficult issues of a formal logical nature, as well as many specific practical problems of system design. An important insight that has resulted from the design of several artificial intelligence systems is that robustness of performance in the presence of many uncertainty relationships can be achieved by eliciting from the expert a segmentation of knowledge that will also provide a rich network of deterministic relationships to interweave the space of hypotheses.