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The AQ21 program aims to perform natural induction, a process of generating inductive hypotheses in humanoriented forms that are easy to interpret and understand. This is achieved by employing a highly expressive representation language, Attributional Calculus, whose statements resemble natural language descriptions. This paper focuses on the Pattern Discovery mode of AQ21, which produces attributional rules that capture strong regularities in the data, but may not be fully consistent or complete with regard to the training data. AQ21 integrates several novel features, such as optimizing patterns according to multiple criteria, learning attributional rules with exceptions, generating optimized sets of alternative hypotheses, and handling data with unknown, irrelevant and/or non-applicable meta-values.