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.
@article{arxiv.1110.2736,
title = {Marvin: A Heuristic Search Planner with Online Macro-Action Learning},
author = {A. I. Coles and A. J. Smith},
journal= {arXiv preprint arXiv:1110.2736},
year = {2011}
}