Capturing and exploiting structural knowledge of
planning problems has shown to be a successful
strategy for making the planning process more ef-
ficient. 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
