LM-cut landmark constraints

Computes a set of landmarks in each state using the LM-cut method. For each landmark L the constraint sum_{o in L} Count_o >= 1 is added to the operator counting LP temporarily. After the heuristic value for the state is computed, all temporary constraints are removed again. For details, see

• Florian Pommerening, Gabriele Roeger, Malte Helmert and Blai Bonet.
  LP-based Heuristics for Cost-optimal Planning.
  In Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014), pp. 226-234. AAAI Press 2014.

• Blai Bonet.
  An admissible heuristic for SAS+ planning obtained from the state equation.
  In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013), pp. 2268-2274. 2013.

lmcut_constraints()

Posthoc optimization constraints for iPDB patterns

A pattern collection is discovered, using iPDB hillclimbing (see [Doc/iPDB]).The generator will compute a PDB for each pattern and add the constraint h(s) <= sum_{o in relevant(h)} Count_o. For details, see

• Florian Pommerening, Gabriele Roeger and Malte Helmert.
  Getting the Most Out of Pattern Databases for Classical Planning.
  In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013), pp. 2357-2364. 2013.

pho_constraints_ipdb(pdb_max_size=2000000, collection_max_size=20000000, num_samples=1000, min_improvement=10, max_time=infinity)

• pdb_max_size (int [1, infinity]): maximal number of states per pattern database

• collection_max_size (int [1, infinity]): maximal number of states in the pattern collection

• num_samples (int [1, infinity]): number of samples (random states) on which to evaluate each candidate pattern collection

• min_improvement (int [1, infinity]): minimum number of samples on which a candidate pattern collection must improve on the current one to be considered as the next pattern collection

• max_time (double [0.0, infinity]): maximum time in seconds for improving the initial pattern collection via hill climbing. If set to 0, no hill climbing is performed at all.

Posthoc optimization constraints for manually specified patterns

The generator will compute a PDB for each pattern and add the constraint h(s) <= sum_{o in relevant(h)} Count_o. For details, see

• Florian Pommerening, Gabriele Roeger and Malte Helmert.
  Getting the Most Out of Pattern Databases for Classical Planning.
  In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013), pp. 2357-2364. 2013.

pho_constraints_manual(patterns=<none>, combo=false, max_states=1000000)

• patterns (list of list of int): list of patterns (which are lists of variable numbers of the planning task). Default: each goal variable is used as a single-variable pattern in the collection.

• combo (bool): use the combo strategy

• max_states (int [1, infinity]): maximum abstraction size for combo strategy

Posthoc optimization constraints for systematically generated patterns

All (interesting) patterns with up to pattern_max_size variables are generated. The generator will compute a PDB for each pattern and add the constraint h(s) <= sum_{o in relevant(h)} Count_o. For details, see

• Florian Pommerening, Gabriele Roeger and Malte Helmert.
  Getting the Most Out of Pattern Databases for Classical Planning.
  In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013), pp. 2357-2364. 2013.

pho_constraints_systematic(pattern_max_size=1, only_interesting_patterns=true)

• pattern_max_size (int): max number of variables per pattern

• only_interesting_patterns (bool): Only consider the union of two disjoint patterns if the union has more information than the individual patterns.

State equation constraints

For each fact, a permanent constraint is added that considers the net change of the fact, i.e., the total number of times the fact is added minus the total number of times is removed. The bounds of each constraint depend on the current state and the goal state and are updated in each state. For details, see

• Menkes van den Briel, J. Benton, Subbarao Kambhampati and Thomas Vossen.
  An LP-based heuristic for optimal planning.
  In Proceedings of the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP 2007), pp. 651-665. 2007.

• Blai Bonet.
  An admissible heuristic for SAS+ planning obtained from the state equation.
  In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013), pp. 2268-2274. 2013.

• Florian Pommerening, Gabriele Roeger, Malte Helmert and Blai Bonet.
  LP-based Heuristics for Cost-optimal Planning.
  In Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014), pp. 226-234. AAAI Press 2014.

state_equation_constraints()

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