# Hierarchical Finite State Controllers for Generalized Planning

arXiv: https://arxiv.org/abs/1911.02887 · HTML: https://mlsanity.com/paper/1911.02887/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Generalized Planning as Heuristic Search](https://mlsanity.com/paper/2103.14434/) (2021)
- [Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects](https://mlsanity.com/paper/2301.11087/) (2023)
- [Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems](https://mlsanity.com/paper/2204.04301/) (2022)
- [Abstracting Situation Calculus Action Theories](https://mlsanity.com/paper/2410.14712/) (2024)

## Abstract (arXiv)

Finite State Controllers (FSCs) are an effective way to represent sequential plans compactly. By imposing appropriate conditions on transitions, FSCs can also represent generalized plans that solve a range of planning problems from a given domain. In this paper we introduce the concept of it hierarchical FSCs for planning by allowing controllers to call other controllers. We show that hierarchical FSCs can represent generalized plans more compactly than individual FSCs. Moreover, our call mechanism makes it possible to generate hierarchical FSCs in a modular fashion, or even to apply recursion. We also introduce a compilation that enables a classical planner to generate hierarchical FSCs that solve challenging generalized planning problems. The compilation takes as input a set of planning problems from a given domain and outputs a single classical planning problem, whose solution corresponds to a hierarchical FSC.