# 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.

## Key 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.

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Source: https://tomesphere.com/paper/2508.21564