# Computing Programs for Generalized Planning as Heuristic Search

arXiv: https://arxiv.org/abs/2205.06259 · HTML: https://mlsanity.com/paper/2205.06259/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 4 of 4)

- [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)
- [Generalized Planning With Procedural Domain Control Knowledge](https://mlsanity.com/paper/1910.04999/) (2019)
- [Generalized Planning with Positive and Negative Examples](https://mlsanity.com/paper/1911.09365/) (2019)

## Built on by (most cited first, 3 of 3)

- [Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects](https://mlsanity.com/paper/2301.11087/) (2023)
- [Representation and Synthesis of C++ Programs for Generalized Planning](https://mlsanity.com/paper/2206.14480/) (2022)
- [Synthesis of Procedural Models for Deterministic Transition Systems](https://mlsanity.com/paper/2307.14368/) (2023)

## 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). 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 program-based solution space for GP that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines the BFGP algorithm for GP, that implements a best-first search in our program-based solution space, and that is guided by different evaluation and heuristic functions.