用自然语言查询约束规划模型，快速找到专家级解法。
CP-Model-Zoo: A Natural Language Query System for Constraint Programming Models
- 基于自然语言描述从专家模型库中检索最匹配的代码
- 在不同水平用户输入下均实现高准确率检索
- 无需人工标注，适合非专家快速解决组合优化问题
约束编程及其高级建模语言长期以来被认为具有解决组合问题的潜力。然而，建模语言复杂、全局约束众多以及构建优质模型的技巧性，常使非专家难以使用约束编程求解实际问题。虽然仅凭自然语言生成专家级模型仍是梦想，但本文提出名为CP-Model-Zoo的辅导系统，利用多年来积累的专家编写模型。该系统根据用户对组合问题的自然语言描述，从数据库中检索最相近的源代码模型，确保呈现给用户的均为经专家验证的高质量模型，且无需人工数据标注。实验表明，在模拟不同水平用户输入的问题描述下，该系统仍能实现出色的模型检索准确性。
原文摘要 · Abstract (English)
Constraint Programming and its high-level modeling languages have long been recognized for their potential to achieve the holy grail of problem-solving. However, the complexity of modeling languages, the large number of global constraints, and the art of creating good models have often hindered non-experts from choosing CP to solve their combinatorial problems. While generating an expert-level model from a natural-language description of a problem would be the dream, we are not yet there. We propose a tutoring system called CP-Model-Zoo, exploiting expert-written models accumulated through the years. CP-Model-Zoo retrieves the closest source code model from a database based on a user's natural language description of a combinatorial problem. It ensures that expert-validated models are presented to the user while eliminating the need for human data labeling. Our experiments show excellent accuracy in retrieving the correct model based on a user-input description of a problem simulated with different levels of expertise.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。