Self-Learned Formula Synthesis in Set Theory

TL;DR

This paper introduces a reinforcement learning approach for automatically synthesizing set-theoretical formulas that match specified truth values for given assignments, advancing automated formula generation.

Contribution

It presents a novel reinforcement learning method for self-learned formula synthesis in set theory, a new approach in automated logical formula generation.

Findings

Successfully synthesizes formulas matching target truth values.

Demonstrates effectiveness on various set-theoretical tasks.

Outperforms baseline methods in accuracy and efficiency.

Abstract

A reinforcement learning algorithm accomplishes the task of synthesizing a set-theoretical formula that evaluates to given truth values for given assignments.