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| Operator-counting constraints based on the delete relaxation. By default the constraints encode an easy-to-compute relaxation of h^+^. If both use_time_vars and use_integer_vars are set to true and this is the only constriant in an operator-counting heursitic with integer variables for the operator counts, the resulting heuristic is the optimal delete-relaxation heuristic h^+^. For details, see | Operator-counting constraints based on the delete relaxation. By default the constraints encode an easy-to-compute relaxation of h^+^. If both use_time_vars and use_integer_vars are set to true and this is the only constraint in an operator-counting heuristic with integer variables for the operator counts, the resulting heuristic is the optimal delete-relaxation heuristic h^+^. For details, see |
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| * ''use_integer_vars'' (bool): restrict auxiliary variables to integer values. These variables encode whether operators are used, facts are reached, which operator first achieves which fact, and in which order the operators are used. Restricting them to integers generally improves the heursitic value at the cost of increased runtime. | * ''use_integer_vars'' (bool): restrict auxiliary variables to integer values. These variables encode whether operators are used, facts are reached, which operator first achieves which fact, and in which order the operators are used. Restricting them to integers generally improves the heuristic value at the cost of increased runtime. |
Contents
Delete relaxation constraints
Operator-counting constraints based on the delete relaxation. By default the constraints encode an easy-to-compute relaxation of h+. If both use_time_vars and use_integer_vars are set to true and this is the only constraint in an operator-counting heuristic with integer variables for the operator counts, the resulting heuristic is the optimal delete-relaxation heuristic h+. For details, see
Tatsuya Imai and Alex Fukunaga.
On a practical, integer-linear programming model for delete-freetasks and its use as a heuristic for cost-optimal planning.
Journal of Artificial Intelligence Research 54:631-677. 2015.
delete_relaxation_constraints(use_time_vars=false, use_integer_vars=false)
use_time_vars (bool): use variables for time steps. With these additional variables the constraints enforce an order between the selected operators. Leaving this off (default) corresponds to the time relaxation by Imai and Fukunaga. Switching it on, can increase the heuristic value but will increase the size of the constraints which has a strong impact on runtime. Constraints involving time variables use a big-M encoding, so they are more useful if used with integer variables.
use_integer_vars (bool): restrict auxiliary variables to integer values. These variables encode whether operators are used, facts are reached, which operator first achieves which fact, and in which order the operators are used. Restricting them to integers generally improves the heuristic value at the cost of increased runtime.
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. AAAI Press, 2013.
lmcut_constraints()
Posthoc optimization constraints
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. AAAI Press, 2013.
pho_constraints(patterns=systematic(2))
patterns (PatternCollectionGenerator): pattern generation method
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. Springer-Verlag, 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. AAAI Press, 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()