Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach

2026-09-10

AI Summary

本文提出了一种神经-符号框架，通过训练神经Oracle Transformer模型来模拟用户响应并泛化知识，减少了用户参与，从而自动学习约束网络。

Abstract

Learning user-defined concepts as constraint networks has been extensively studied in the constraint acquisition (CA) literature. However, existing approaches typically rely on intensive interactions with a human oracle, making the learning process costly in terms of time and number of queries. In this paper, we propose a neuro-symbolic framework for automatic CA that significantly reduces user involvement by introducing neural Oracle Transformer models which learn to emulate user responses and to generalize conceptual knowledge. Trained on previously available examples, the learned oracle interacts with a dedicated CA engine, FastCA, which systematically refines the oracle's responses into a sound, consistent, and interpretable constraint network. This neuro-symbolic interaction enables the recovery of structured symbolic models from data without prior domain knowledge. Our results demonstrate that this neuro-symbolic interplay effectively aligns data-driven pattern recognition with symbolic reasoning, offering a robust approach to automating model construction in combinatorial domains.

Problem

• constraint acquisition
• user involvement
• neuro-symbolic
• data-driven
• symbolic reasoning

Innovation

• neuro-symbolic framework
• Oracle Transformer models
• constraint acquisition
• FastCA
• data-driven pattern recognition