Marvin: A Heuristic Search Planner with Online Macro-Action Learning
Andrew Coles andrew.coles@cis.strath.ac.uk
Amanda Smith amanda.smith@cis.strath.ac.uk
Department of Computer and Information Sciences,
University of Strathclyde,
26
Richmond Street, Glasgow, G1 1XH, UK
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.
Marvin's Search Behaviour
Handling ADL
Handling Derived Predicates
Results
Future Work
Conclusions
Acknowledgements
Bibliography
About this document ...
Andrew Coles and Amanda Smith
2007-01-09