# Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects

arXiv: https://arxiv.org/abs/2301.11087 · HTML: https://mlsanity.com/paper/2301.11087/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 132 references that are in the map.
- First posted on arXiv 26 Jan 2023.

## Builds on (most cited first, 21 of 21)

- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Learning Generalized Reactive Policies using Deep Neural Networks](https://mlsanity.com/paper/1708.07280/) (2017)
- [Learning Features and Abstract Actions for Computing Generalized Plans](https://mlsanity.com/paper/1811.07231/) (2018)
- [Action Schema Networks: Generalised Policies with Deep Learning](https://mlsanity.com/paper/1709.04271/) (2017)
- [Few-Shot Bayesian Imitation Learning with Logical Program Policies](https://mlsanity.com/paper/1904.06317/) (2019)
- [Features, Projections, and Representation Change for Generalized Planning](https://mlsanity.com/paper/1801.10055/) (2018)
- [Learning Generalized Policies Without Supervision Using GNNs](https://mlsanity.com/paper/2205.06002/) (2022)
- [Qualitative Numeric Planning: Reductions and Complexity](https://mlsanity.com/paper/1912.04816/) (2019)
- [Generalized Planning as Heuristic Search](https://mlsanity.com/paper/2103.14434/) (2021)
- [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)
- [Compiling Uncertainty Away in Conformant Planning Problems with Bounded Width](https://mlsanity.com/paper/1401.3468/) (2014)
- [Generalized Planning With Procedural Domain Control Knowledge](https://mlsanity.com/paper/1910.04999/) (2019)
- [Learning General Policies from Small Examples Without Supervision](https://mlsanity.com/paper/2101.00692/) (2021)
- [Deep Policies for Width-Based Planning in Pixel Domains](https://mlsanity.com/paper/1904.07091/) (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)
- [Computing Programs for Generalized Planning as Heuristic Search](https://mlsanity.com/paper/2205.06259/) (2022)
- [Scaling up Heuristic Planning with Relational Decision Trees](https://mlsanity.com/paper/1401.3885/) (2014)
- [General Policies, Serializations, and Planning Width](https://mlsanity.com/paper/2012.08033/) (2020)

## Built on by (most cited first, 6 of 6)

- [Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code](https://mlsanity.com/paper/2503.18809/) (2025)
- [Planning-Driven Programming: A Large Language Model Programming Workflow](https://mlsanity.com/paper/2411.14503/) (2024)
- [Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies](https://mlsanity.com/paper/2508.18507/) (2025)
- [Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study](https://mlsanity.com/paper/2508.18515/) (2025)
- [Planning with Dynamically Changing Domains](https://mlsanity.com/paper/2508.02697/) (2025)
- [Parallel Strategies for Best-First Generalized Planning](https://mlsanity.com/paper/2407.21485/) (2024)

## Abstract (arXiv)

Planning as heuristic search is one of the most successful approaches to classical planning but unfortunately, it does not extend trivially to Generalized Planning (GP). GP aims to compute algorithmic solutions that are valid for a set of classical planning instances from a given domain, even if these instances differ in the number of objects, the number of state variables, their domain size, or their initial and goal configuration. The generalization requirements of GP make it impractical to perform the state-space search that is usually implemented by heuristic planners. This paper adapts the planning as heuristic search paradigm to the generalization requirements of GP, and presents the first native heuristic search approach to GP. First, the paper introduces a new pointer-based solution space for GP that is independent of the number of classical planning instances in a GP problem and the size of those instances (i.e. the number of objects, state variables and their domain sizes). Second, the paper defines a set of evaluation and heuristic functions for guiding a combinatorial search in our new GP solution space. The computation of these evaluation and heuristic functions does not require grounding states or actions in advance. Therefore our GP as heuristic search approach can handle large sets of state variables with large numerical domains, e.g.~integers. Lastly, the paper defines an upgraded version of our novel algorithm for GP called Best-First Generalized Planning (BFGP), that implements a best-first search in our pointer-based solution space, and that is guided by our evaluation/heuristic functions for GP.