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| [[LP-based Heuristics for Cost-optimal Planning|http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7892/8031]].<<BR>> In ''Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014)'', pp. 226-234. AAAI Press 2014. |
[[http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7892/8031|LP-based Heuristics for Cost-optimal Planning]].<<BR>> In ''Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014)'', pp. 226-234. AAAI Press, 2014. |
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| [[An admissible heuristic for SAS+ planning obtained from the state equation|http://ijcai.org/papers13/Papers/IJCAI13-335.pdf]].<<BR>> In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2268-2274. 2013. |
[[http://ijcai.org/papers13/Papers/IJCAI13-335.pdf|An admissible heuristic for SAS+ planning obtained from the state equation]].<<BR>> In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2268-2274. AAAI Press, 2013. |
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| [[Getting the Most Out of Pattern Databases for Classical Planning|http://ijcai.org/papers13/Papers/IJCAI13-347.pdf]].<<BR>> In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2357-2364. 2013. |
[[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. AAAI Press, 2013. |
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| [[An LP-based heuristic for optimal planning|http://link.springer.com/chapter/10.1007/978-3-540-74970-7_46]].<<BR>> In ''Proceedings of the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP 2007)'', pp. 651-665. 2007. |
[[http://link.springer.com/chapter/10.1007/978-3-540-74970-7_46|An LP-based heuristic for optimal planning]].<<BR>> In ''Proceedings of the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP 2007)'', pp. 651-665. Springer-Verlag, 2007. |
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| [[An admissible heuristic for SAS+ planning obtained from the state equation|http://ijcai.org/papers13/Papers/IJCAI13-335.pdf]].<<BR>> In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2268-2274. 2013. |
[[http://ijcai.org/papers13/Papers/IJCAI13-335.pdf|An admissible heuristic for SAS+ planning obtained from the state equation]].<<BR>> In ''Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013)'', pp. 2268-2274. AAAI Press, 2013. |
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| [[LP-based Heuristics for Cost-optimal Planning|http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7892/8031]].<<BR>> In ''Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014)'', pp. 226-234. AAAI Press 2014. |
[[http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7892/8031|LP-based Heuristics for Cost-optimal Planning]].<<BR>> In ''Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014)'', pp. 226-234. AAAI Press, 2014. |
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()