Marvin: A Heuristic Search Planner with Online Macro-Action Learning
Abstract
This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate macro-actions, which are then used during search for a solution plan. We provide an overview of its architecture and search behaviour, detailing the algorithms used. We also empirically demonstrate the effectiveness of its features in various planning domains; in particular, the effects on performance due to the use of macro-actions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates.
Cite
Text
Coles and Smith. "Marvin: A Heuristic Search Planner with Online Macro-Action Learning." Journal of Artificial Intelligence Research, 2007. doi:10.1613/JAIR.2077Markdown
[Coles and Smith. "Marvin: A Heuristic Search Planner with Online Macro-Action Learning." Journal of Artificial Intelligence Research, 2007.](https://mlanthology.org/jair/2007/coles2007jair-marvin/) doi:10.1613/JAIR.2077BibTeX
@article{coles2007jair-marvin,
title = {{Marvin: A Heuristic Search Planner with Online Macro-Action Learning}},
author = {Coles, Andrew and Smith, Amanda},
journal = {Journal of Artificial Intelligence Research},
year = {2007},
pages = {119-156},
doi = {10.1613/JAIR.2077},
volume = {28},
url = {https://mlanthology.org/jair/2007/coles2007jair-marvin/}
}