# Hierarchical Finite State Controllers for Generalized Planning

@inproceedings{Aguas2016HierarchicalFS, title={Hierarchical Finite State Controllers for Generalized Planning}, author={Javier Segovia Aguas and Sergio Jim{\'e}nez Celorrio and Anders Jonsson}, booktitle={IJCAI}, year={2016} }

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 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…

## 14 Citations

Computing Hierarchical Finite State Controllers With Classical Planning

- Computer ScienceJ. Artif. Intell. Res.
- 2018

A classical planning compilation for computing hierarchical FSCs that solve challenging generalized planning tasks and allows controllers to call other controllers to represent generalized plans more compactly than individual F SCs.

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An algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) is proposed and the unique difficulties that arise when forced to consider geometric and physical constraints as a part of the generalized plan are examined.

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- 2019

The paper reviews recent advances in generalized planning and relates them to existing planning formalisms, such as planning with domain control knowledge and approaches for planning under uncertainty, that also aim at generality.

Verifiable Parameterised Behaviour Models - For Robotic and Embedded Systems

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- 2018

This work produces safe mechanisms to set actual and formal parameters for multiple, concurrent instances of the same behaviour, and achieves the parameterisation of behaviour models analogous to a procedural abstraction.

Generalized Planning as Heuristic Search

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- 2021

This paper presents a novel GP solution space that is independent of the number of planning instances in a GP problem, and the size of these instances, and adapts the planning as heuristic search paradigm to the particularities of GP.

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- 2022

This paper proposes an approach that uses canonical abstractions to compute generalized policies and represents them as AND-OR graphs that translate to simple non-deterministic, memoryless con- trollers and shows that it is promising, often computing optimal policies signiﬁcantly faster than state-of-art SSP solvers.

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- 2017

A general framework for agent abstraction based on the situation calculus and the ConGolog agent programming language is developed and the notion of complete abstraction where all actions that the high level thinks can happen can in fact occur at the low level is characterized.

A I ] 2 6 M ar 2 02 1 Generalized Planning as Heuristic Search

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This 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, and presents the first native heuristic search approach to GP.

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