• Corpus ID: 85517762

Towards Machine Learning Induction.

@article{Nagashima2018TowardsML,
  title={Towards Machine Learning Induction.},
  author={Yutaka Nagashima},
  journal={arXiv: Logic in Computer Science},
  year={2018}
}
  • Yutaka Nagashima
  • Published 4 December 2018
  • Computer Science
  • arXiv: Logic in Computer Science
Induction lies at the heart of mathematics and computer science. However, automated theorem proving of inductive problems is still limited in its power. In this abstract, we first summarize our progress in automating inductive theorem proving for Isabelle/HOL. Then, we present MeLoId, our approach to suggesting promising applications of induction without completing a proof search. 
1 Citations
Towards United Reasoning for Automatic Induction in Isabelle/HOL
TLDR
The recent developments of proof by induction for Isabelle/HOL are summarized and united reasoning, a novel approach to further automating inductive theorem proving, is proposed, which takes the best of three schools of reasoning: deductive reasoning, inductive reasoning), to prove difficult inductive problems automatically.

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  • Yutaka Nagashima, Yilun He
  • Computer Science
    2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE)
  • 2018
TLDR
PaMpeR, a proof method recommendation system for Isabelle/HOL, correctly predicts experienced users' proof methods invocation, especially when it comes to special purpose proof methods.
A Proof Assistant for Higher-Order Logic