A review of generalized planning
By Sergio Jiménez Celorrio, Javier Segovia Aguas, Anders Jonsson. 2019
62 citations recorded by the source corpus.
Abstract Generalized planning studies the representation, computation and evaluation of solutions that are valid for multiple planning instances. These are topics studied since the early days of AI. However, in recent years, we are experiencing the appearance of novel formalisms to compactly represent generalized planning tasks, the solutions to these tasks (called generalized plans) and efficient algorithms to compute generalized plans. 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.
Subfields: Artificial Intelligence, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Josh Tenenbaum
- Sergey Levine
- Leslie Pack Kaelbling
- Tomas Lozano-Perez
- Denny Zhou
- Tom Silver
- Lihong Li
- Jiayuan Mao
In Reinforcement Learning
- Josh Tenenbaum
- Sergey Levine
- Denny Zhou
- Lihong Li
- Tomas Lozano-Perez
- Leslie Pack Kaelbling
- Tom Silver
- Jiayuan Mao
Citations in the corpus
- Neural Logic Machines (2019, methodology): “General planning methods solve these tasks via planning based on rules (Hu & De Giacomo, 2011; Srivastava et al., 2011; Jiménez et al., 2018).”
- Discovering State and Action Abstractions for Generalized Task and Motion Planning (2021, methodology): “Generalized planning for purely discrete domains has been the subject of much prior work (Jiménez, Segovia-Aguas, and Jonsson 2019).”