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| stubborn_sets_ec() | stubborn_sets_ec(min_required_pruning_ratio=0.0, expansions_before_checking_pruning_ratio=1000) |
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| * ''min_required_pruning_ratio'' (double [0.0, 1.0]): disable pruning if the pruning ratio is lower than this value after 'expansions_before_checking_pruning_ratio' expansions * ''expansions_before_checking_pruning_ratio'' (int [0, infinity]): number of expansions before deciding whether to disable pruning '''Automatically disable pruning:''' Using stubborn sets to prune operators often reduces the required number of expansions but computing the prunable operators has a non-negligible runtime overhead. Whether the decrease in expansions outweighs the increased computational costs depends on the task at hand. Using the options 'min_required_pruning_ratio' (M) and 'expansions_before_checking_pruning_ratio' (E) it is possible to automatically disable pruning after E expansions if the ratio of pruned vs. non-pruned operators is lower than M. In detail, let B and A be the total number of operators before and after pruning summed over all previous expansions. We call 1-(A/B) the pruning ratio R. If R is lower than M after E expansions, we disable pruning for all subsequent expansions, i.e., consider all applicable operators when generating successor states. By default, pruning is never disabled (min_required_pruning_ratio = 0.0). In experiments on IPC benchmarks, stronger results have been observed with automatic disabling (min_required_pruning_ratio = 0.2, expansions_before_checking_pruning_ratio=1000). |
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| stubborn_sets_simple() | stubborn_sets_simple(min_required_pruning_ratio=0.0, expansions_before_checking_pruning_ratio=1000) |
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* ''min_required_pruning_ratio'' (double [0.0, 1.0]): disable pruning if the pruning ratio is lower than this value after 'expansions_before_checking_pruning_ratio' expansions * ''expansions_before_checking_pruning_ratio'' (int [0, infinity]): number of expansions before deciding whether to disable pruning '''Automatically disable pruning:''' Using stubborn sets to prune operators often reduces the required number of expansions but computing the prunable operators has a non-negligible runtime overhead. Whether the decrease in expansions outweighs the increased computational costs depends on the task at hand. Using the options 'min_required_pruning_ratio' (M) and 'expansions_before_checking_pruning_ratio' (E) it is possible to automatically disable pruning after E expansions if the ratio of pruned vs. non-pruned operators is lower than M. In detail, let B and A be the total number of operators before and after pruning summed over all previous expansions. We call 1-(A/B) the pruning ratio R. If R is lower than M after E expansions, we disable pruning for all subsequent expansions, i.e., consider all applicable operators when generating successor states. By default, pruning is never disabled (min_required_pruning_ratio = 0.0). In experiments on IPC benchmarks, stronger results have been observed with automatic disabling (min_required_pruning_ratio = 0.2, expansions_before_checking_pruning_ratio=1000). |
Contents
Prune or reorder applicable operators.
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()
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
Martin Wehrle, Malte Helmert, Yusra Alkhazraji and Robert Mattmueller.
The Relative Pruning Power of Strong Stubborn Sets and Expansion Core.
In Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS 2013), pp. 251-259. AAAI Press 2013.
stubborn_sets_ec(min_required_pruning_ratio=0.0, expansions_before_checking_pruning_ratio=1000)
min_required_pruning_ratio (double [0.0, 1.0]): disable pruning if the pruning ratio is lower than this value after 'expansions_before_checking_pruning_ratio' expansions
expansions_before_checking_pruning_ratio (int [0, infinity]): number of expansions before deciding whether to disable pruning
Automatically disable pruning: Using stubborn sets to prune operators often reduces the required number of expansions but computing the prunable operators has a non-negligible runtime overhead. Whether the decrease in expansions outweighs the increased computational costs depends on the task at hand. Using the options 'min_required_pruning_ratio' (M) and 'expansions_before_checking_pruning_ratio' (E) it is possible to automatically disable pruning after E expansions if the ratio of pruned vs. non-pruned operators is lower than M. In detail, let B and A be the total number of operators before and after pruning summed over all previous expansions. We call 1-(A/B) the pruning ratio R. If R is lower than M after E expansions, we disable pruning for all subsequent expansions, i.e., consider all applicable operators when generating successor states. By default, pruning is never disabled (min_required_pruning_ratio = 0.0). In experiments on IPC benchmarks, stronger results have been observed with automatic disabling (min_required_pruning_ratio = 0.2, expansions_before_checking_pruning_ratio=1000).
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:
Yusra Alkhazraji, Martin Wehrle, Robert Mattmueller and Malte Helmert.
A Stubborn Set Algorithm for Optimal Planning.
In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), pp. 891-892. IOS Press 2012.Martin Wehrle and Malte Helmert.
Efficient Stubborn Sets: Generalized Algorithms and Selection Strategies.
In Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS 2014), pp. 323-331. AAAI Press, 2014.
stubborn_sets_simple(min_required_pruning_ratio=0.0, expansions_before_checking_pruning_ratio=1000)
min_required_pruning_ratio (double [0.0, 1.0]): disable pruning if the pruning ratio is lower than this value after 'expansions_before_checking_pruning_ratio' expansions
expansions_before_checking_pruning_ratio (int [0, infinity]): number of expansions before deciding whether to disable pruning
Automatically disable pruning: Using stubborn sets to prune operators often reduces the required number of expansions but computing the prunable operators has a non-negligible runtime overhead. Whether the decrease in expansions outweighs the increased computational costs depends on the task at hand. Using the options 'min_required_pruning_ratio' (M) and 'expansions_before_checking_pruning_ratio' (E) it is possible to automatically disable pruning after E expansions if the ratio of pruned vs. non-pruned operators is lower than M. In detail, let B and A be the total number of operators before and after pruning summed over all previous expansions. We call 1-(A/B) the pruning ratio R. If R is lower than M after E expansions, we disable pruning for all subsequent expansions, i.e., consider all applicable operators when generating successor states. By default, pruning is never disabled (min_required_pruning_ratio = 0.0). In experiments on IPC benchmarks, stronger results have been observed with automatic disabling (min_required_pruning_ratio = 0.2, expansions_before_checking_pruning_ratio=1000).