Dynamic Tree Databases in Automated Planning
This work addresses a scalability problem in automated planning, offering an incremental improvement over existing tree databases by making them dynamic.
The paper tackles the challenge of compactly representing generated states in explicit state-space search for large planning tasks by proposing a dynamic variant of tree databases, achieving compression ratios of several orders of magnitude with negligible runtime overhead.
A central challenge in scaling up explicit state-space search for large tasks is compactly representing the set of generated states. Tree databases, a data structure from model checking, require constant space per generated state in the best case, but they need a large preallocation of memory. We propose a novel dynamic variant of tree databases for compressing state sets over propositional and numeric variables and prove that it maintains the desirable properties of the static counterpart. Our empirical evaluation of state compression techniques for grounded and lifted planning on classical and numeric planning tasks reveals compression ratios of several orders of magnitude, often with negligible runtime overhead.