|
Size: 5855
Comment:
|
Size: 5012
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 2: | Line 2: |
== 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.<<BR>> [[https://www.jair.org/index.php/jair/article/download/10972/26119/|On a practical, integer-linear programming model for delete-freetasks and its use as a heuristic for cost-optimal planning]].<<BR>> ''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)) }}} |
|
| Line 5: | Line 24: |
| 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 | 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 |
| Line 8: | Line 28: |
| In ''Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014)'', pp. 226-234. AAAI Press 2014. | In ''Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014)'', pp. 226-234. AAAI Press, 2014. |
| Line 11: | Line 32: |
| In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2268–2274. 2013. | In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2268-2274. AAAI Press, 2013. |
| Line 17: | Line 38: |
| == Posthoc optimization constraints for iPDB patterns == | == Posthoc optimization constraints == |
| Line 19: | Line 40: |
| A pattern collection is discovered, using iPDB hillclimbing (see [Doc/[[Doc/Heuristic#iPDB|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 | 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 |
| Line 22: | Line 44: |
| In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2357-2364. 2013. | In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2357-2364. AAAI Press, 2013. |
| Line 25: | Line 47: |
| pho_constraints_ipdb(pdb_max_size=2000000, collection_max_size=20000000, num_samples=1000, min_improvement=10, max_time=infinity) | pho_constraints(patterns=systematic(2)) |
| Line 28: | Line 50: |
| * ''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.<<BR>> [[http://ijcai.org/papers13/Papers/IJCAI13-347.pdf|Getting the Most Out of Pattern Databases for Classical Planning]].<<BR>> 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.<<BR>> [[http://ijcai.org/papers13/Papers/IJCAI13-347.pdf|Getting the Most Out of Pattern Databases for Classical Planning]].<<BR>> 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. |
* ''patterns'' ([[Doc/PatternCollectionGenerator|PatternCollectionGenerator]]): pattern generation method |
| Line 60: | Line 53: |
| Line 62: | Line 56: |
| In ''Proceedings of the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP 2007)'', pp. 651–665. 2007. | In ''Proceedings of the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP 2007)'', pp. 651-665. Springer-Verlag, 2007. |
| Line 65: | Line 60: |
| In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2268–2274. 2013. | In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2268-2274. AAAI Press, 2013. |
| Line 68: | Line 64: |
| In ''Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014)'', pp. 226-234. AAAI Press 2014. | In ''Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014)'', pp. 226-234. AAAI Press, 2014. |
| Line 74: | Line 70: |
| /* moin code generated by txt2tags 2.6b (http://txt2tags.sf.net) */ | /* moin code generated by txt2tags 2.6 (http://txt2tags.org) */ |
Contents
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()