# The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks

arXiv: https://arxiv.org/abs/1401.3839 · HTML: https://mlsanity.com/paper/1401.3839/

## Key facts

- Cited by 87 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2014.

## Built on by (most cited first, 30 of 87)

- [Generalized Planning in PDDL Domains with Pretrained Large Language Models](https://mlsanity.com/paper/2305.11014/) (2023)
- [Heuristic-Guided Reinforcement Learning](https://mlsanity.com/paper/2106.02757/) (2021)
- [Learning Domain-Independent Heuristics for Grounded and Lifted Planning](https://mlsanity.com/paper/2312.11143/) (2023)
- [Classical Planning in Deep Latent Space](https://mlsanity.com/paper/2107.00110/) (2021)
- [NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptions](https://mlsanity.com/paper/2405.04215/) (2024)
- [Policy-Guided Heuristic Search with Guarantees](https://mlsanity.com/paper/2103.11505/) (2021)
- [Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning](https://mlsanity.com/paper/2403.16508/) (2024)
- [Generalized Planning as Heuristic Search](https://mlsanity.com/paper/2103.14434/) (2021)
- [Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code](https://mlsanity.com/paper/2503.18809/) (2025)
- [Scaling-up Generalized Planning as Heuristic Search with Landmarks](https://mlsanity.com/paper/2205.04850/) (2022)
- [Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Version](https://mlsanity.com/paper/2203.14852/) (2022)
- [Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search](https://mlsanity.com/paper/2206.00702/) (2022)
- [Expressing and Exploiting the Common Subgoal Structure of Classical Planning Domains Using Sketches: Extended Version](https://mlsanity.com/paper/2105.04250/) (2021)
- [Goal Recognition as Reinforcement Learning](https://mlsanity.com/paper/2202.06356/) (2022)
- [Goal Recognition as a Deep Learning Task: the GRNet Approach](https://mlsanity.com/paper/2210.02377/) (2022)
- [Learning Graph Search Heuristics](https://mlsanity.com/paper/2212.03978/) (2022)
- [Frontier Large Language Models Rival State-of-the-Art Planners](https://mlsanity.com/paper/2511.09378/) (2025)
- [Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning](https://mlsanity.com/paper/2112.01918/) (2021)
- [NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning Problems](https://mlsanity.com/paper/2301.10280/) (2023)
- [Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects](https://mlsanity.com/paper/2301.11087/) (2023)
- [General Policies, Subgoal Structure, and Planning Width](https://mlsanity.com/paper/2311.05490/) (2023)
- [Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning](https://mlsanity.com/paper/2404.05235/) (2024)
- [Novelty and Lifted Helpful Actions in Generalized Planning](https://mlsanity.com/paper/2307.00735/) (2023)
- [Symmetry-Aware Transformer Training for Automated Planning](https://mlsanity.com/paper/2508.07743/) (2025)
- [Approximate Novelty Search](https://mlsanity.com/paper/2105.07691/) (2021)
- [STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent](https://mlsanity.com/paper/2310.01775/) (2023)
- [What's the Plan? Evaluating and Developing Planning-Aware Techniques for Language Models](https://mlsanity.com/paper/2402.11489/) (2024)
- [Leveraging Action Relational Structures for Integrated Learning and Planning](https://mlsanity.com/paper/2504.20318/) (2025)
- [Sampling from Pre-Images to Learn Heuristic Functions for Classical Planning](https://mlsanity.com/paper/2207.03336/) (2022)
- [PushWorld: A benchmark for manipulation planning with tools and movable obstacles](https://mlsanity.com/paper/2301.10289/) (2023)

## Abstract (arXiv)

LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudo-heuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finite-domain rather than binary state variables and multi-heuristic search. The latter is employed to combine the landmark heuristic with a variant of the well-known FF heuristic. Both heuristics are cost-sensitive, focusing on high-quality solutions in the case where actions have non-uniform cost. A weighted A* search is used with iteratively decreasing weights, so that the planner continues to search for plans of better quality until the search is terminated. LAMA showed best performance among all planners in the sequential satisficing track of the International Planning Competition 2008. In this paper we present the system in detail and investigate which features of LAMA are crucial for its performance. We present individual results for some of the domains used at the competition, demonstrating good and bad cases for the techniques implemented in LAMA. Overall, we find that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial. We show that in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future. The iterated weighted A* search greatly improves results, and shows synergy effects with the use of landmarks.