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:
Raz Nissim, Joerg Hoffmann and Malte Helmert.
Computing Perfect Heuristics in Polynomial Time: On Bisimulation and Merge-and-Shrink Abstractions in Optimal Planning..
In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 1983-1990. 2011.
shrink_bisimulation(greedy=false, at_limit=RETURN)
greedy (bool): use greedy bisimulation
at_limit ({RETURN, USE_UP}): what to do when the size limit is hit
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:
Malte Helmert, Patrik Haslum and Joerg Hoffmann.
Flexible Abstraction Heuristics for Optimal Sequential Planning.
In Proceedings of the Seventeenth International Conference on Automated Planning and Scheduling (ICAPS 2007), pp. 176-183. 2007.
shrink_fh(random_seed=-1, shrink_f=HIGH, shrink_h=LOW)
random_seed (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.
shrink_f ({HIGH, LOW}): prefer shrinking states with high or low f values
shrink_h ({HIGH, LOW}): prefer shrinking states with high or low h values
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).
Random
shrink_random(random_seed=-1)
random_seed (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.