Feature‐based learning of search‐guiding heuristics for theorem proving

  • Authors:
  • Marc Fuchs;Matthias Fuchs

  • Affiliations:
  • Fakultä/t fü/r Informatik, TU Mü/nchen, 80290 Mü/nchen, Germany (Correspd. Tel. +49 89 289 27916/ Fax. +49 89 289 27902/ fuchsm&commat/in.tum.de;Automated Reasoning Project, RSISE, Australian National University, Canberra ACT 0200, Australia

  • Venue:
  • AI Communications
  • Year:
  • 1998

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Abstract

Automated reasoning or theorem proving essentially amounts to solving search problems. Despite significant progress in recent years theorem provers still have many shortcomings. The use of machine‐learning techniques is acknowledged as promising, but difficult to apply in the area of theorem proving. We propose here to learn search‐guiding heuristics by employing features in a simple, yet effective manner. Features are used to adapt a heuristic to a solved source problem. The adapted heuristic can then be utilized profitably for solving related target problems. Experiments have demonstrated that the approach allows a theorem prover to prove hard problems that were out of reach before.