Revisiting Landmarks: Learning from Previous Plans to Generalize over Problem Instances

TL;DR

This paper introduces a framework for learning generalized landmarks from solved instances that can be applied across a domain, improving planning efficiency especially with repetitive subplans.

Contribution

It presents a novel method for extracting domain-independent landmarks using state functions, enabling better generalization and heuristic performance in planning.

Findings

Generalized landmarks improve planning for larger instances.

Loop detection enhances heuristic effectiveness.

Few small instances suffice for learning effective landmarks.

Abstract

We propose a new framework for discovering landmarks that automatically generalize across a domain. These generalized landmarks are learned from a set of solved instances and describe intermediate goals for planning problems where traditional landmark extraction algorithms fall short. Our generalized landmarks extend beyond the predicates of a domain by using state functions that are independent of the objects of a specific problem and apply to all similar objects, thus capturing repetition. Based on these functions, we construct a directed generalized landmark graph that defines the landmark progression, including loop possibilities for repetitive subplans. We show how to use this graph in a heuristic to solve new problem instances of the same domain. Our results show that the generalized landmark graphs learned from a few small instances are also effective for larger instances in the same domain. If a loop that indicates repetition is identified, we see a significant improvement in heuristic performance over the baseline. Generalized landmarks capture domain information that is interpretable and useful to an automated planner. This information can be discovered from a small set of plans for the same domain.