Static learning for an adaptative theorem prover

  • C. Belleannee
  • J. Nicolas
Part 4: Theorem Proving And EBL
Part of the Lecture Notes in Computer Science book series (LNCS, volume 482)


An adaptative theorem prover is a system able to modify its current set of inference rules in order to improve its performance on a specific domain. We address here the issue of the generation of inference rules, without considering the selection and deletion issues. We especially develop the treatment of repeating events within a proof. We specify a general representation for objects to be learned in this framework, that is macro-connectives and macro-inference-rules and show how they may be generated from the primitive set of inference rules. Our main contribution consists to show that a form of analytical, static learning, is possible in this domain.


Theorem proving Macro-operators Sequent calculus Generalization to N 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • C. Belleannee
    • 1
  • J. Nicolas
    • 1
  1. 1.Irisa-InriaRennes cedexFrance

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