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Learning domain knowledge to improve theorem proving

  • Jörg Denzinger
  • Stephan Schulz
Session 1B
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1104)

Abstract

We present two learning inference control heuristics for equational deduction. Based on data about facts that contributed to previous proofs, evaluation functions learn to select equations that are likely to be of use in new situations. The first evaluation function works by symbolic retrieval of generalized patterns from a knowledge base, the second function compiles the knowledge into abstract term evaluation trees. We analyze the performance of the two heuristics on a set of examples and demonstrate their usefulness. We also show that these strategies are well suited for cooperation in the framework of the knowledge based distribution method teamwork.

Keywords

Learning Strategy Proof System Reproductive Mode Critical Pair Learning Team 
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 1996

Authors and Affiliations

  • Jörg Denzinger
    • 1
  • Stephan Schulz
    • 2
  1. 1.Fachbereich InformatikUniversität KaiserslauternKaiserslauternGermany
  2. 2.Institut für InformatikTechnische Universität MünchenMünchenGermany

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