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Capturing and exploiting structural knowledge of planning problems has shown to be a successful strategy for making the planning process more efficient. Plans can be decomposed into its constituent coherent subplans, called blocks, that encapsulate some effects and preconditions, reducing interference and thus allowing more deordering of plans. According to the nature of blocks, they can be straightforwardly transformed into useful macro-operators (shortly, "macros"). Macros are well known and widely studied kind of structural knowledge because they can be easily encoded in the domain model and thus exploited by standard planning engines. In this paper, we introduce a method, called BLOMA, that learns domain-specific macros from plans, decomposed into "macro-blocks" which are extensions of blocks, utilising structural knowledge they capture. In contrast to existing macro learning techniques, macro-blocks are often able to capture high-level activities that form a basis for useful longer macros (i.e. those consisting of more original operators). Our method is evaluated by using the IPC benchmarks with state-of-the-art planning engines, and shows considerable improvement in many cases.
|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 - 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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