Abstract
The notion of an inductive inference machine aggregating a team of inference machines models the problem of making use of several explanations for a single phenomenon. This article investigates the amount of information necessary for a successful aggregation of the theories given by a team of inference machines. Variations of using different kinds of identification and aggregation are investigated.
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