Marvin: a heuristic search planner with online macro-action learning

Authors:
Andrew Coles;Amanda Smith
Affiliations:
Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK;Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
Venue:
Journal of Artificial Intelligence Research
Year:
2007

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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.