Revision 4 as of 2014-01-27 18:12:55

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A landmark graph specification is either a newly created instance or a landmark graph that has been defined previously. This page describes how one can specify a new landmark graph instance. For re-using landmark graphs, see Landmark Predefinitions.

Warning: See OptionCaveats for using cost types with Landmarks

Exhaustive Landmarks

Exhaustively checks for each fact if it is a landmark.This check is done using relaxed planning.

lm_exhaust(cost_type=NORMAL, reasonable_orders=false, only_causal_landmarks=false, disjunctive_landmarks=true, conjunctive_landmarks=true, no_orders=false, lm_cost_type=NORMAL)

Relevant options: reasonable_orders, only_causal_landmarks

h^m Landmarks

The landmark generation method introduced by Keyder, Richter & Helmert (ECAI 2010).

lm_hm(m=2, cost_type=NORMAL, reasonable_orders=false, only_causal_landmarks=false, disjunctive_landmarks=true, conjunctive_landmarks=true, no_orders=false, lm_cost_type=NORMAL)

Relevant options: m, reasonable_orders, conjunctive_landmarks, no_orders

Merged Landmarks

Merges the landmarks and orderings from the parameter landmarks

lm_merged(lm_graphs, cost_type=NORMAL, reasonable_orders=false, only_causal_landmarks=false, disjunctive_landmarks=true, conjunctive_landmarks=true, no_orders=false, lm_cost_type=NORMAL)

Precedence: Fact landmarks take precedence over disjunctive landmarks, orderings take precedence in the usual manner (gn > nat > reas > o_reas).

Relevant options: Depends on landmarks

Note: Does not currently support conjunctive landmarks

RHW Landmarks

The landmark generation method introduced by Richter, Helmert and Westphal (AAAI 2008).

lm_rhw(cost_type=NORMAL, reasonable_orders=false, only_causal_landmarks=false, disjunctive_landmarks=true, conjunctive_landmarks=true, no_orders=false, lm_cost_type=NORMAL)

Relevant Options: reasonable_orders, only_causal_landmarks, disjunctive_landmarks, no_orders

Zhu/Givan Landmarks

The landmark generation method introduced by Zhu & Givan (ICAPS 2003 Doctoral Consortium).

lm_zg(cost_type=NORMAL, reasonable_orders=false, only_causal_landmarks=false, disjunctive_landmarks=true, conjunctive_landmarks=true, no_orders=false, lm_cost_type=NORMAL)

Relevant options: reasonable_orders, no_orders