Delete relaxation constraints

Operator-counting constraints based on the delete relaxation. By default the constraints encode an easy-to-compute relaxation of h⁺. With the right settings, these constraints can be used to compute the optimal delete-relaxation heuristic h⁺ (see example below). 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.

Example: To compute the optimal delete-relaxation heuristic h⁺, use

operatorcounting([delete_relaxation_constraints(use_time_vars=true, use_integer_vars=true)], use_integer_operator_counts=true))

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(verbosity=normal)

• verbosity ({silent, normal, verbose, debug}): Option to specify the verbosity level.

  • silent: only the most basic output

  • normal: relevant information to monitor progress

  • verbose: full output

  • debug: like verbose with additional debug output

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