用逻辑规则注入背景知识，让模型快速学会高质量规划。
Deep Learning for Generalised Planning with Background Knowledge
- 通过Datalog规则注入背景知识，引导学习与规划过程
- 仅用5秒生成小数据集，即实现高效高质量规划
- 适合需要快速适配新任务的自动化规划场景
自动化规划是一种声明式问题求解方法，近年来受到机器学习领域的关注。机器学习常被用于测试模型的推理能力，或借助学习到的领域知识提升求解器的可扩展性。然而，实践中规划问题易解但难优化，现有机器学习方法仍难以解决许多对人类和经典规划器而言简单的任务。本文提出一种新方法，允许用户通过Datalog规则指定背景知识（BK），以集成方式指导学习与规划过程。引入背景知识后，模型无需从零开始重新学习，而是聚焦于计划质量的优化。实验表明，该方法在引入背景知识后，能快速扩展并从小规模训练数据中学习到高效的高质量规划策略，训练数据生成时间不足5秒。
原文摘要 · Abstract (English)
Automated planning is a form of declarative problem solving which has recently drawn attention from the machine learning (ML) community. ML has been applied to planning either as a way to test `reasoning capabilities' of architectures, or more pragmatically in an attempt to scale up solvers with learned domain knowledge. In practice, planning problems are easy to solve but hard to optimise. However, ML approaches still struggle to solve many problems that are often easy for both humans and classical planners. In this paper, we thus propose a new ML approach that allows users to specify background knowledge (BK) through Datalog rules to guide both the learning and planning processes in an integrated fashion. By incorporating BK, our approach bypasses the need to relearn how to solve problems from scratch and instead focuses the learning on plan quality optimisation. Experiments with BK demonstrate that our method successfully scales and learns to plan efficiently with high quality solutions from small training data generated in under 5 seconds.
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