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(max_states=-1, max_states_before_merge=-1, threshold=-1, greedy=false, at_limit=RETURN)
max_states (int): maximum transition system size allowed at any time point.
max_states_before_merge (int): maximum transition system size allowed for two transition systems before being merged to form the synchronized product.
threshold (int): If a transition system, before being merged, surpasses this soft transition system size limit, the shrink strategy is called to possibly shrink the transition system.
greedy (bool): use greedy bisimulation
at_limit ({RETURN, USE_UP}): what to do when the size limit is hit
shrink_bisimulation(max_states=infinity, threshold=1, greedy=true): Greedy bisimulation without size bound (called M&S-gop in the IJCAI 2011 paper).Combine this with the linear merge strategy REVERSE_LEVEL to match the heuristic in the 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(max_states=N, greedy=false): Exact bisimulation with a size limit (called DFP-bop in the IJCAI 2011 paper), where N is a numerical parameter for which sensible values include 1000, 10000, 50000, 100000 and 200000. Combine this with the linear merge strategy REVERSE_LEVEL to match the heuristic in the 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(max_states=-1, max_states_before_merge=-1, threshold=-1, shrink_f=HIGH, shrink_h=LOW)
max_states (int): maximum transition system size allowed at any time point.
max_states_before_merge (int): maximum transition system size allowed for two transition systems before being merged to form the synchronized product.
threshold (int): If a transition system, before being merged, surpasses this soft transition system size limit, the shrink strategy is called to possibly shrink the transition system.
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(max_states=N): f-preserving shrinking of transition systems (called HHH in the IJCAI 2011 paper, see shrink_bisimulation). Here, N is a numerical parameter for which sensible values include 1000, 10000, 50000, 100000 and 200000. Combine this with the linear merge strategy CG_GOAL_LEVEL to match the heuristic in the paper. 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(max_states=-1, max_states_before_merge=-1, threshold=-1)
max_states (int): maximum transition system size allowed at any time point.
max_states_before_merge (int): maximum transition system size allowed for two transition systems before being merged to form the synchronized product.
threshold (int): If a transition system, before being merged, surpasses this soft transition system size limit, the shrink strategy is called to possibly shrink the transition system.