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Alchemist: Learning Guarded Affine Functions

  • Shambwaditya SahaEmail author
  • Pranav GargEmail author
  • P. MadhusudanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9206)

Abstract

We present a technique and an accompanying tool that learns guarded affine functions. In our setting, a teacher starts with a guarded affine function and the learner learns this function using equivalence queries only. In each round, the teacher examines the current hypothesis of the learner and gives a counter-example in terms of an input-output pair where the hypothesis differs from the target function. The learner uses these input-output pairs to learn the guarded affine expression. This problem is relevant in synthesis domains where we are trying to synthesize guarded affine functions that have particular properties, provided we can build a teacher who can answer using such counter-examples. We implement our approach and show that our learner is effective in learning guarded affine expressions, and more effective than general-purpose synthesis techniques.

Keywords

Target Function Numerical Attribute Affine Function Linear Expression Leaf Plane 
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.

Notes

Acknowledgments

We thank Sariel Har-Peled for discussions on geometric techniques for synthesizing leaf expressions. This work was partially supported by NSF Expeditions in Computing ExCAPE Award #1138994.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of IllinoisUrbana-ChampaignUSA

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