Prune or reorder applicable operators.

Atom-centric stubborn sets

Stubborn sets are a state pruning method which computes a subset of applicable actions in each state such that completeness and optimality of the overall search is preserved. Previous stubborn set implementations mainly track information about actions. In contrast, this implementation focuses on atomic propositions (atoms), which often speeds up the computation on IPC benchmarks. For details, see

atom_centric_stubborn_sets(use_sibling_shortcut=true, atom_selection_strategy=quick_skip, verbosity=normal)

Note on verbosity parameter: Setting verbosity to verbose or higher enables time measurements in each call to prune_operators for a given state. This induces a significant overhead, up to 30% in configurations like blind search with the no pruning method (null). We recommend using at most normal verbosity for running experiments.

Limited pruning

Limited pruning applies another pruning method and switches it off after a fixed number of expansions if the pruning ratio is below a given value. The pruning ratio is the sum of all pruned operators divided by the sum of all operators before pruning, considering all previous expansions.

limited_pruning(pruning, min_required_pruning_ratio=0.2, expansions_before_checking_pruning_ratio=1000, verbosity=normal)

Example: To use atom centric stubborn sets and limit them, use

pruning=limited_pruning(pruning=atom_centric_stubborn_sets(),min_required_pruning_ratio=0.2,expansions_before_checking_pruning_ratio=1000)

in an eager search such as astar.

Note on verbosity parameter: Setting verbosity to verbose or higher enables time measurements in each call to prune_operators for a given state. This induces a significant overhead, up to 30% in configurations like blind search with the no pruning method (null). We recommend using at most normal verbosity for running experiments.

No pruning

This is a skeleton method that does not perform any pruning, i.e., all applicable operators are applied in all expanded states.

null(verbosity=normal)

Note on verbosity parameter: Setting verbosity to verbose or higher enables time measurements in each call to prune_operators for a given state. This induces a significant overhead, up to 30% in configurations like blind search with the no pruning method (null). We recommend using at most normal verbosity for running experiments.

StubbornSetsEC

Stubborn sets represent a state pruning method which computes a subset of applicable operators in each state such that completeness and optimality of the overall search is preserved. As stubborn sets rely on several design choices, there are different variants thereof. The variant 'StubbornSetsEC' resolves the design choices such that the resulting pruning method is guaranteed to strictly dominate the Expansion Core pruning method. For details, see

stubborn_sets_ec(verbosity=normal)

Note on verbosity parameter: Setting verbosity to verbose or higher enables time measurements in each call to prune_operators for a given state. This induces a significant overhead, up to 30% in configurations like blind search with the no pruning method (null). We recommend using at most normal verbosity for running experiments.

Stubborn sets simple

Stubborn sets represent a state pruning method which computes a subset of applicable operators in each state such that completeness and optimality of the overall search is preserved. As stubborn sets rely on several design choices, there are different variants thereof. The variant 'StubbornSetsSimple' resolves the design choices in a straight-forward way. For details, see the following papers:

stubborn_sets_simple(verbosity=normal)

Note on verbosity parameter: Setting verbosity to verbose or higher enables time measurements in each call to prune_operators for a given state. This induces a significant overhead, up to 30% in configurations like blind search with the no pruning method (null). We recommend using at most normal verbosity for running experiments.

FastDownward: Doc/PruningMethod (last edited 2022-07-15 12:42:37 by XmlRpcBot)