# Generalized Planning as Heuristic Search

arXiv: https://arxiv.org/abs/2103.14434 · HTML: https://mlsanity.com/paper/2103.14434/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Learning Features and Abstract Actions for Computing Generalized Plans](https://mlsanity.com/paper/1811.07231/) (2018)
- [Qualitative Numeric Planning: Reductions and Complexity](https://mlsanity.com/paper/1912.04816/) (2019)
- [Generalized Planning with Positive and Negative Examples](https://mlsanity.com/paper/1911.09365/) (2019)
- [Hierarchical Finite State Controllers for Generalized Planning](https://mlsanity.com/paper/1911.02887/) (2019)
- [Heuristics for Planning, Plan Recognition and Parsing](https://mlsanity.com/paper/1605.05807/) (2016)

## Built on by (most cited first, 18 of 18)

- [Generalized Planning in PDDL Domains with Pretrained Large Language Models](https://mlsanity.com/paper/2305.11014/) (2023)
- [ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning](https://mlsanity.com/paper/2308.13724/) (2023)
- [MedDM:LLM-executable clinical guidance tree for clinical decision-making](https://mlsanity.com/paper/2312.02441/) (2023)
- [Scaling-up Generalized Planning as Heuristic Search with Landmarks](https://mlsanity.com/paper/2205.04850/) (2022)
- [PG3: Policy-Guided Planning for Generalized Policy Generation](https://mlsanity.com/paper/2204.10420/) (2022)
- [Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects](https://mlsanity.com/paper/2301.11087/) (2023)
- [Novelty and Lifted Helpful Actions in Generalized Planning](https://mlsanity.com/paper/2307.00735/) (2023)
- [Graph Learning for Numeric Planning](https://mlsanity.com/paper/2410.24080/) (2024)
- [Hierarchical Decomposition and Analysis for Generalized Planning](https://mlsanity.com/paper/2212.02823/) (2022)
- [Computing Programs for Generalized Planning as Heuristic Search](https://mlsanity.com/paper/2205.06259/) (2022)
- [Deep Learning for Generalised Planning with Background Knowledge](https://mlsanity.com/paper/2410.07923/) (2024)
- [Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study](https://mlsanity.com/paper/2508.18515/) (2025)
- [Representation and Synthesis of C++ Programs for Generalized Planning](https://mlsanity.com/paper/2206.14480/) (2022)
- [On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies](https://mlsanity.com/paper/2403.16824/) (2024)
- [Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning](https://mlsanity.com/paper/2208.02938/) (2022)
- [Synthesis of Procedural Models for Deterministic Transition Systems](https://mlsanity.com/paper/2307.14368/) (2023)
- [Parallel Strategies for Best-First Generalized Planning](https://mlsanity.com/paper/2407.21485/) (2024)
- [On Sample-Efficient Generalized Planning via Learned Transition Models](https://mlsanity.com/paper/2602.23148/) (2026)

## Abstract (arXiv)

Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). Planning as heuristic search traditionally addresses the computation of sequential plans by searching in a grounded state-space. On the other hand GP aims at computing algorithm-like plans, that can branch and loop, and that generalize to a (possibly infinite) set of classical planning instances. This paper adapts the planning as heuristic search paradigm to the particularities of GP, and presents the first native heuristic search approach to GP. First, the paper defines a novel GP solution space that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines different evaluation and heuristic functions for guiding a combinatorial search in our GP solution space. Lastly the paper defines a GP algorithm, called Best-First Generalized Planning (BFGP), that implements a best-first search in the solution space guided by our evaluation/heuristic functions.