It is difficult to assess hypothetical models in poorly measured domains such as neuroendocrinology. Without a large library of observations to constrain inference, the execution of such incomplete models implies making assumptions. Mutually exclusive assumptions must be kept in separate worlds. We define a general abductive multiple-worlds engine that assesses such models by (i) generating the worlds and (ii) tests if these worlds contain known behaviour. World generation is constrained via the use of relevant envisionment. We describe QCM, a modeling language for compartmental models that can be processed by this inference engine. This tool has been used to nd faults in theories published in international refereed journals; i.e. QCM can detect faults which are invisible to other methods. The generality and computational limits of this approach are discussed. In short, this approach is applicable to any representation that can be compiled into an and-or graph, provided the graphs are not too big or too intricate (fanout<7)
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