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Abstract Learning Frameworks for Synthesis

  • Christof Löding
  • P. Madhusudan
  • Daniel Neider
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9636)

Abstract

We develop abstract learning frameworks for synthesis that embody the principles of the CEGIS (counterexample-guided inductive synthesis) algorithms in current literature. Our framework is based on iterative learning from a hypothesis space that captures synthesized objects, using counterexamples from an abstract sample space, and a concept space that abstractly defines the semantics of synthesis. We show that a variety of synthesis algorithms in current literature can be embedded in this general framework. We also exhibit three general recipes for convergent synthesis: the first two recipes based on finite spaces and Occam learners generalize all techniques of convergence used in existing engines, while the third, involving well-founded quasi-orderings, is new, and we instantiate it to concrete synthesis problems.

Notes

Acknowledgements

This work was partially supported by NSF Expeditions in Computing ExCAPE Award #1138994.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Christof Löding
    • 1
  • P. Madhusudan
    • 2
  • Daniel Neider
    • 2
    • 3
  1. 1.RWTH AachenAachenGermany
  2. 2.University of Illinois, Urbana-ChampaignChampaignUSA
  3. 3.University of CaliforniaLos AngelesUSA

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