Marvin: A Heuristic Search Planner with Online Macro-Action Learning
Andrew Coles firstname.lastname@example.org
Amanda Smith email@example.com
Department of Computer and Information Sciences,
University of Strathclyde,
Richmond Street, Glasgow, G1 1XH, UK
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.
Andrew Coles and Amanda Smith