This page describes the various shrink strategies supported by the planner.

Bismulation based shrink strategy

This shrink strategy implements the algorithm described in the paper:

shrink_bisimulation(greedy=false, at_limit=RETURN)

shrink_bisimulation(greedy=true): Combine this with the merge-and-shrink options max_states=infinity and threshold_before_merge=1 and with the linear merge strategy reverse_level to obtain the variant 'greedy bisimulation without size limit', called M&S-gop in the IJCAI 2011 paper. When we last ran experiments on interaction of shrink strategies with label reduction, this strategy performed best when used with label reduction before shrinking (and no label reduction before merging).

shrink_bisimulation(greedy=false): Combine this with the merge-and-shrink option max_states=N (where N is a numerical parameter for which sensible values include 1000, 10000, 50000, 100000 and 200000) and with the linear merge strategy reverse_level to obtain the variant 'exact bisimulation with a size limit', called DFP-bop in the IJCAI 2011 paper. When we last ran experiments on interaction of shrink strategies with label reduction, this strategy performed best when used with label reduction before shrinking (and no label reduction before merging).

f-preserving shrink strategy

This shrink strategy implements the algorithm described in the paper:

shrink_fh(random_seed=-1, shrink_f=HIGH, shrink_h=LOW)

shrink_fh(): Combine this with the merge-and-shrink option max_states=N (where N is a numerical parameter for which sensible values include 1000, 10000, 50000, 100000 and 200000) and the linear merge startegy cg_goal_level to obtain the variant 'f-preserving shrinking of transition systems', called called HHH in the IJCAI 2011 paper, see bisimulation based shrink strategy. When we last ran experiments on interaction of shrink strategies with label reduction, this strategy performed best when used with label reduction before merging (and no label reduction before shrinking). We also recommend using full pruning with this shrink strategy, because both distances from the initial state and to the goal states must be computed anyway, and because the existence of only one dead state causes this shrink strategy to always use the map-based approach for partitioning states rather than the more efficient vector-based approach.

Random

shrink_random(random_seed=-1)

FastDownward: Doc/ShrinkStrategy (last edited 2017-07-09 13:49:48 by XmlRpcBot)