Computer Science – Artificial Intelligence
Scientific paper
2011-10-12
Journal Of Artificial Intelligence Research, Volume 28, pages 119-156, 2007
Computer Science
Artificial Intelligence
Scientific paper
10.1613/jair.2077
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.
Coles A. I.
Smith Anthony J.
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