Martin Pollet

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In this paper we present an approach to automated learning within mathematical reasoning systems. In particular, the approach enables proof planning systems to automatically learn new proof methods from well-chosen examples of proofs which use a similar reasoning pattern to prove related theorems. Our approach consists of an abstract representation for(More)
We describe the integration of permutation group algorithms with proof planning. We consider eight basic questions arising in computational permutation group theory, for which our code provides both answers and a set of certificates enabling a user, or an intelligent software system, to provide a full proof of correctness of the answer. To guarantee(More)
In a randomized field experiment, we investigate the interplay between work goals, monetary incentives, and work performance. Employees are observed in a natural work environment where they have to do a simple, effort-intense task. Output is perfectly observable and workers are paid for performance. While a regular piece-rate contract serves as a benchmark,(More)