通过破除逻辑假设空间对称性，显著加速归纳逻辑编程求解。
Symmetry breaking for inductive logic programming
- 利用答案集编程破除等价假设的对称性，缩小搜索空间。
- 在视觉推理与游戏领域，求解时间从超一小时缩短至17秒。
- 适合需要高效逻辑推理的AI系统研发者参考。
归纳逻辑编程的目标是寻找能泛化训练数据和背景知识的假设。挑战在于搜索庞大的假设空间，且存在大量逻辑等价的假设。为应对这一难题，我们提出一种破除假设空间对称性的方法，并在答案集编程中实现。在多个领域（包括视觉推理与游戏博弈）的实验表明，该方法可将求解时间从超过一小时缩短至仅17秒。
原文摘要 · Abstract (English)
The goal of inductive logic programming is to search for a hypothesis that generalises training data and background knowledge. The challenge is searching vast hypothesis spaces, which is exacerbated because many logically equivalent hypotheses exist. To address this challenge, we introduce a method to break symmetries in the hypothesis space. We implement our idea in answer set programming. Our experiments on multiple domains, including visual reasoning and game playing, show that our approach can reduce solving times from over an hour to just 17 seconds.
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