Overview and Evaluation of Premise Selection Techniques for Large Theory Mathematics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7364)


In this paper, an overview of state-of-the-art techniques for premise selection in large theory mathematics is provided, and new premise selection techniques are introduced. Several evaluation metrics are introduced, compared and their appropriateness is discussed in the context of automated reasoning in large theory mathematics. The methods are evaluated on the MPTP2078 benchmark, a subset of the Mizar library, and a 10% improvement is obtained over the best method so far.


Prediction Function Evaluation Metrics Latent Semantic Analysis Automate Reasoning Random Projection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Radboud UniversityNijmegenNetherlands

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