# Relevance Score: A Landmark-Like Heuristic for Planning

arXiv: https://arxiv.org/abs/2403.07510 · HTML: https://mlsanity.com/paper/2403.07510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Landmark-Based Approaches for Goal Recognition as Planning](https://mlsanity.com/paper/1904.11739/) (2019)
- [Scaling-up Generalized Planning as Heuristic Search with Landmarks](https://mlsanity.com/paper/2205.04850/) (2022)
- [REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics](https://mlsanity.com/paper/1508.03891/) (2015)

## Built on by (most cited first, 1 of 1)

- [From domain-landmark graph learning to problem-landmark graph generation](https://mlsanity.com/paper/2509.17062/) (2025)

## Abstract (arXiv)

Landmarks are facts or actions that appear in all valid solutions of a planning problem. They have been used successfully to calculate heuristics that guide the search for a plan. We investigate an extension to this concept by defining a novel "relevance score" that helps identify facts or actions that appear in most but not all plans to achieve any given goal. We describe an approach to compute this relevance score and use it as a heuristic in the search for a plan. We experimentally compare the performance of our approach with that of a state of the art landmark-based heuristic planning approach using benchmark planning problems. While the original landmark-based heuristic leads to better performance on problems with well-defined landmarks, our approach substantially improves performance on problems that lack non-trivial landmarks.