A review of learning planning action models

By Ankuj Arora, Humbert Fiorino, Damien Pellier, Marc Métivier, Sylvie Pesty. 2018

112 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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

1. Hector Geffner
2. Tomas Lozano-Perez
3. Leslie Pack Kaelbling
4. Josh Tenenbaum
5. Blai Bonet
6. Subbarao Kambhampati
7. Tom Silver
8. Sarath Sreedharan

In Information Systems

1. Blai Bonet
2. Subbarao Kambhampati
3. Josh Tenenbaum
4. Hector Geffner

Citations in the corpus

• Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, background): “2 Related Research Object-oriented MDPs [13] and similar work in classical planning [37, 2, 1], build first-order model representations but starting with a first-order symbolic language, or with information about the actions and their arguments [11].”
• Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning (2022, methodology): “Our operator learning method is largely based on prior work [20, 27, 28].”
• Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning (2023, background): “For a more comprehensive survey, we refer the reader to [2, 6].”