# Features, Projections, and Representation Change for Generalized Planning

arXiv: https://arxiv.org/abs/1801.10055 · HTML: https://mlsanity.com/paper/1801.10055/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2018.

## Built on by (most cited first, 25 of 25)

- [Generalized Planning in PDDL Domains with Pretrained Large Language Models](https://mlsanity.com/paper/2305.11014/) (2023)
- [Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits](https://mlsanity.com/paper/2109.10129/) (2021)
- [Learning Generalized Policies Without Supervision Using GNNs](https://mlsanity.com/paper/2205.06002/) (2022)
- [Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Version](https://mlsanity.com/paper/2203.14852/) (2022)
- [Expressing and Exploiting the Common Subgoal Structure of Classical Planning Domains Using Sketches: Extended Version](https://mlsanity.com/paper/2105.04250/) (2021)
- [PG3: Policy-Guided Planning for Generalized Policy Generation](https://mlsanity.com/paper/2204.10420/) (2022)
- [Learning General Policies from Small Examples Without Supervision](https://mlsanity.com/paper/2101.00692/) (2021)
- [Symmetries and Expressive Requirements for Learning General Policies](https://mlsanity.com/paper/2409.15892/) (2024)
- [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)
- [Target Languages (vs. Inductive Biases) for Learning to Act and Plan](https://mlsanity.com/paper/2109.07195/) (2021)
- [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)
- [Deep Learning for Generalised Planning with Background Knowledge](https://mlsanity.com/paper/2410.07923/) (2024)
- [Sketch Decompositions for Classical Planning via Deep Reinforcement Learning](https://mlsanity.com/paper/2412.08574/) (2024)
- [Automatic Verification of Sound Abstractions for Generalized Planning](https://mlsanity.com/paper/2205.11898/) (2022)
- [On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies](https://mlsanity.com/paper/2403.16824/) (2024)
- [Learning Generalized Policies for Fully Observable Non-Deterministic Planning Domains](https://mlsanity.com/paper/2404.02499/) (2024)
- [Abstracting Situation Calculus Action Theories](https://mlsanity.com/paper/2410.14712/) (2024)
- [An Automatic Sound and Complete Abstraction Method for Generalized Planning with Baggable Types](https://mlsanity.com/paper/2501.15249/) (2025)
- [Provably Complete Generalized Planning with LLMs](https://mlsanity.com/paper/2609.27105/) (2026)
- [Language-Based Causal Representation Learning](https://mlsanity.com/paper/2207.05259/) (2022)
- [Revisiting Landmarks: Learning from Previous Plans to Generalize over Problem Instances](https://mlsanity.com/paper/2508.21564/) (2025)
- [Learning General Policies From Examples](https://mlsanity.com/paper/2509.02794/) (2025)
- [Abstraction Generation for Generalized Planning with Pretrained Large Language Models](https://mlsanity.com/paper/2602.10485/) (2026)

## Abstract (arXiv)

Generalized planning is concerned with the characterization and computation of plans that solve many instances at once. In the standard formulation, a generalized plan is a mapping from feature or observation histories into actions, assuming that the instances share a common pool of features and actions. This assumption, however, excludes the standard relational planning domains where actions and objects change across instances. In this work, we extend the standard formulation of generalized planning to such domains. This is achieved by projecting the actions over the features, resulting in a common set of abstract actions which can be tested for soundness and completeness, and which can be used for generating general policies such as "if the gripper is empty, pick the clear block above x and place it on the table" that achieve the goal clear(x) in any Blocksworld instance. In this policy, "pick the clear block above x" is an abstract action that may represent the action Unstack(a, b) in one situation and the action Unstack(b, c) in another. Transformations are also introduced for computing such policies by means of fully observable non-deterministic (FOND) planners. The value of generalized representations for learning general policies is also discussed.