Abstract
Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be utilized for a case-based ranking and adaptation of similar cases. We introduce the system OASES and its application to medical experimental studies to demonstrate this approach.
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