July 31, 2025

Automated reasoning boosted by a probabilistic automaton classifier: An application to the Andrews-Curtis conjecture

Key Points

Main finding shows that using a probabilistic automaton classifier significantly boosts automated reasoning efficiency.
Key evidence from test cases related to the Andrews-Curtis conjecture illustrates the effectiveness of the proposed method.
Approach integrates supervised learning techniques to effectively train the automaton classifier for task identification.
Significance lies in providing a new method that can reduce time spent on complex reasoning tasks within mathematical conjectures.

Abstract

Abstract Automated reasoning can take a prohibitively long time to run. We present a combined approach which makes automated reasoning more efficient by using supervised learning to identify promising tasks for automated reasoning. For supervised learning, we have developed a new, simple and efficient way of training an automaton classifier. We demonstrate the efficiency of our approach on examples drawn from the Andrews-Curtis conjecture, a famously challenging problem.

Ask AI

Mark Helpful

Bookmark

Relay