Deep Learning for Generalised Planning with Background Knowledge

TL;DR

This paper introduces a novel machine learning approach for generalized planning that incorporates background knowledge via Datalog rules, enabling efficient learning and high-quality solutions with minimal training data.

Contribution

It presents a new ML method that integrates background knowledge into planning, improving scalability and solution quality compared to existing approaches.

Findings

Successfully scales with small training data

Learns to plan efficiently with high-quality solutions

Training completes in under 5 seconds

Abstract

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.