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Macro-operator ("macro", for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training.
|Title of host publication||IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Number of pages||7|
|Publication status||Published - 25 Jul 2015|
|Event||24th International Joint Conference on Artificial Intelligence - Buenos Aires, Argentina|
Duration: 25 Jul 2015 → 31 Jul 2015
Conference number: 24
http://www.ijcai.org/past_conferences (Link to Conference Website )
|Conference||24th International Joint Conference on Artificial Intelligence|
|Abbreviated title||IJCAI 2015|
|Period||25/07/15 → 31/07/15|
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