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Machine Learning Guidance for Connection Tableaux.

Michael Färber1, Cezary Kaliszyk1, Josef Urban2

  • 1University of Innsbruck, Innsbruck, Austria.

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Summary
This summary is machine-generated.

Connection tableaux offer compact proof search implementations. This work details optimized functional implementations and machine learning guidance methods, including Naive Bayesian probabilities and Monte Carlo Tree Search for enhanced proof search.

Keywords:
Connection tableauxInternal guidanceMonte Carlo

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Area of Science:

  • Automated reasoning
  • Logic and computation

Background:

  • Connection calculi provide efficient goal-directed proof search.
  • Connection tableaux are a specific type of connection calculus.

Purpose of the Study:

  • To present optimized functional implementations of connection tableaux proof search.
  • To introduce machine learning-based guidance methods for proof search.

Main Methods:

  • Optimized functional implementations of connection tableaux.
  • A consistent Skolemisation procedure tailored for machine learning.
  • Naive Bayesian probabilities for reordering proof steps.
  • Monte Carlo Tree Search for expanding the proof search tree.

Main Results:

  • Demonstration of optimized functional implementations for connection tableaux.
  • Successful integration of a consistent Skolemisation procedure.
  • Effective application of Naive Bayesian probabilities for proof step reordering.
  • Efficient proof search tree expansion using Monte Carlo Tree Search.

Conclusions:

  • Optimized implementations and machine learning guidance enhance connection tableaux proof search.
  • The presented methods offer advancements in automated reasoning and theorem proving.