Learning Search Control Knowledge for Equational Theorem Proving

  • Stephan Schulz
Conference paper

DOI: 10.1007/3-540-45422-5_23

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2174)
Cite this paper as:
Schulz S. (2001) Learning Search Control Knowledge for Equational Theorem Proving. In: Baader F., Brewka G., Eiter T. (eds) KI 2001: Advances in Artificial Intelligence. KI 2001. Lecture Notes in Computer Science, vol 2174. Springer, Berlin, Heidelberg

Abstract

One of the major problems in clausal theorem proving is the control of the proof search. In the presence of equality, this problem is particularly hard, since nearly all state-of-the-art systems perform the proof search by saturating a mostly unstructured set of clauses. We describe an approach that enables a superposition-based prover to pick good clauses for generating inferences based on experiences from previous successful proof searches for other problems. Information about good and bad search decisions (useful and superfluous clauses) is automatically collected from search protocols and represented in the form of annotated clause patterns. At run time, new clauses are compared with stored patterns and evaluated according to the associated information found. We describe our implementation of the system. Experimental results demonstrate that a learned heuristic significantly outperforms the conventional base strategy, especially in domains where enough training examples are available.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Stephan Schulz
    • 1
  1. 1.Institut für InformatikTechnische Universität MünchenMünchenGermany

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