Help: Experimental automatically generated documentation.
scalar evaluators
add
add(cost_type = NORMAL)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
blind
blind(cost_type = NORMAL)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
cea
cea(cost_type = NORMAL)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
cg
cg(cost_type = NORMAL)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
ff
ff(cost_type = NORMAL)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
g
g()
goalcount
goalcount(cost_type = NORMAL)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
hm
hm(m = 2, cost_type = NORMAL)
m (int):
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
hmax
hmax(cost_type = NORMAL)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
lmcount
lmcount(lm_graph, admissible = false, optimal = false, pref = false, alm = true, cost_type = NORMAL)
lm_graph (landmarks graph):
admissible (bool):get admissible estimate
optimal (bool):optimal cost sharing
pref (bool):identify preferred operators
alm (bool):use action landmarks
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
lmcut
lmcut(cost_type = NORMAL)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
mas
mas(max_states = -1, max_states_before_merge = -1, count = 1, merge_strategy = MERGE_LINEAR_CG_GOAL_LEVEL, shrink_strategy = SHRINK_HIGH_F_LOW_H, simplify_labels = true, expensive_statistics = false, merge_mixing_parameter = -1, cost_type = NORMAL)
max_states (int):maximum abstraction size
max_states_before_merge (int):maximum abstraction size for factors of synchronized product
count (int):nr of abstractions to build
merge_strategy ({MERGE_LINEAR_CG_GOAL_LEVEL, MERGE_LINEAR_CG_GOAL_RANDOM, MERGE_LINEAR_GOAL_CG_LEVEL, MERGE_LINEAR_RANDOM, MERGE_DFP, MERGE_LINEAR_LEVEL, MERGE_LINEAR_REVERSE_LEVEL, MERGE_LEVEL_THEN_INVERSE, MERGE_INVERSE_THEN_LEVEL}):merge strategy
shrink_strategy ({SHRINK_HIGH_F_LOW_H, SHRINK_LOW_F_LOW_H, SHRINK_HIGH_F_HIGH_H, SHRINK_RANDOM, SHRINK_DFP, SHRINK_BISIMULATION, SHRINK_BISIMULATION_NO_MEMORY_LIMIT, SHRINK_DFP_ENABLE_GREEDY_BISIMULATION, SHRINK_DFP_ENABLE_FURTHER_LABEL_REDUCTION, SHRINK_DFP_ENABLE_GREEDY_THEN_LABEL_REDUCTION, SHRINK_DFP_ENABLE_LABEL_REDUCTION_THEN_GREEDY, SHRINK_DFP_ENABLE_LABEL_REDUCTION_AND_GREEDY_CHOOSE_MAX, SHRINK_GREEDY_BISIMULATION_NO_MEMORY_LIMIT, SHRINK_BISIMULATION_REDUCING_ALL_LABELS_NO_MEMORY_LIMIT, SHRINK_GREEDY_BISIMULATION_REDUCING_ALL_LABELS_NO_MEMORY_LIMIT}):shrink strategy
simplify_labels (bool):enable label simplification
expensive_statistics (bool):show statistics on "unique unlabeled edges" (WARNING: these are *very* slow -- check the warning in the output)
merge_mixing_parameter (double):merge mixing parameter
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
max
max(heuristics, cost_type = NORMAL)
heuristics (list of heuristic):
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
pref
pref()
selmax
selmax(heuristics, alpha = 1, classifier = NB, conf_threshold = 0.6, training_set = 100, eval_always = 0, random_sel = false, retime = false, sample = Probe, uniform = false, zero_threshold = false, cost_type = NORMAL)
heuristics (list of heuristic):
alpha (double):alpha
classifier ({NB, AODE}):classifier type
conf_threshold (double):confidence threshold
training_set (int):minimum size of training set
eval_always (int):number of heuristics that should always be evaluated
random_sel (bool):random selection
retime (bool):retime heuristics
sample ({Probe, ProbAStar, PDB}):state space sample type
uniform (bool):uniform sampling
zero_threshold (bool):set threshold constant 0
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
sum
sum(evals)
evals (list of scalar evaluator):
weight
weight(evals, weight)
evals (list of scalar evaluator):
weight (int):
openlists
alt
alt(sublists, boost = 0)
sublists (list of openlist):
boost (int):boost value for preferred operator open lists
pareto
pareto(evals, pref_only = false, state_uniform_selection = false)
evals (list of scalar evaluator):
pref_only (bool):insert only preferred operators
state_uniform_selection (bool):select uniformly from the candidate *states*
single
single(evaluators, pref_only = false)
evaluators (list of scalar evaluator):
pref_only (bool):insert only preferred operators
single_buckets
single_buckets(evals, pref_only = false)
evals (list of scalar evaluator):
pref_only (bool):insert only preferred operators
tiebreaking
tiebreaking(evals, pref_only = false, unsafe_pruning = true)
evals (list of scalar evaluator):
pref_only (bool):insert only preferred operators
unsafe_pruning (bool):allow unsafe pruning when the main evaluator regards a state a dead end
search engines
astar
astar(eval, pathmax = false, mpd = false, cost_type = NORMAL, bound = 2147483647)
eval (scalar evaluator):
pathmax (bool):use pathmax correction
mpd (bool):use multi-path dependence (LM-A*)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
bound (int):bound on plan cost
eager
eager(open, reopen_closed = false, pathmax = false, f_eval = 0, preferred = [], cost_type = NORMAL, bound = 2147483647)
open (openlist):
reopen_closed (bool):reopen closed nodes
pathmax (bool):use pathmax correction
f_eval (scalar evaluator):set evaluator for jump statistics
preferred (list of heuristic):use preferred operators of these heuristics
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
bound (int):bound on plan cost
eager_greedy
eager_greedy(evals, preferred = [], boost = 0, cost_type = NORMAL, bound = 2147483647)
evals (list of scalar evaluator):
preferred (list of heuristic):use preferred operators of these heuristics
boost (int):boost value for preferred operator open lists
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
bound (int):bound on plan cost
ehc
ehc(h, bfs_use_cost = false, preferred_usage = PRUNE_BY_PREFERRED, preferred = [], cost_type = NORMAL, bound = 2147483647)
h (heuristic):
bfs_use_cost (bool):use cost for bfs
preferred_usage ({PRUNE_BY_PREFERRED, RANK_PREFERRED_FIRST}):preferred operator usage
preferred (list of heuristic):use preferred operators of these heuristics
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
bound (int):bound on plan cost
iterated
iterated(engine_configs, pass_bound = true, repeat_last = false, continue_on_fail = false, continue_on_solve = true, plan_counter = 0, cost_type = NORMAL, bound = 2147483647)
engine_configs (list of parse tree (this just means the input is parsed at a later point. The real type is probably a search engine.)):
pass_bound (bool):use bound from previous search
repeat_last (bool):repeat last phase of search
continue_on_fail (bool):continue search after no solution found
continue_on_solve (bool):continue search after solution found
plan_counter (int):start enumerating plans with this number
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
bound (int):bound on plan cost
lazy
lazy(open, reopen_closed = false, preferred = [], cost_type = NORMAL, bound = 2147483647)
open (openlist):
reopen_closed (bool):reopen closed nodes
preferred (list of heuristic):use preferred operators of these heuristics
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
bound (int):bound on plan cost
lazy_greedy
lazy_greedy(evals, preferred = [], reopen_closed = false, boost = 1000, cost_type = NORMAL, bound = 2147483647)
evals (list of scalar evaluator):
preferred (list of heuristic):use preferred operators of these heuristics
reopen_closed (bool):reopen closed nodes
boost (int):boost value for preferred operator open lists
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
bound (int):bound on plan cost
lazy_wastar
lazy_wastar(evals, preferred = [], reopen_closed = true, boost = 1000, w = 1, cost_type = NORMAL, bound = 2147483647)
evals (list of scalar evaluator):
preferred (list of heuristic):use preferred operators of these heuristics
reopen_closed (bool):reopen closed nodes
boost (int):boost value for preferred operator open lists
w (int):heuristic weight
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
bound (int):bound on plan cost
landmarks graphs
lm_exhaust
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)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
reasonable_orders (bool):generate reasonable orders
only_causal_landmarks (bool):keep only causal landmarks
disjunctive_landmarks (bool):keep disjunctive landmarks
conjunctive_landmarks (bool):keep conjunctive landmarks
no_orders (bool):discard all orderings
lm_cost_type ({NORMAL, ONE, PLUSONE}):landmark action cost adjustment
lm_hm
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)
m (int):m (as in h^m)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
reasonable_orders (bool):generate reasonable orders
only_causal_landmarks (bool):keep only causal landmarks
disjunctive_landmarks (bool):keep disjunctive landmarks
conjunctive_landmarks (bool):keep conjunctive landmarks
no_orders (bool):discard all orderings
lm_cost_type ({NORMAL, ONE, PLUSONE}):landmark action cost adjustment
lm_merged
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)
lm_graphs (list of landmarks graph):
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
reasonable_orders (bool):generate reasonable orders
only_causal_landmarks (bool):keep only causal landmarks
disjunctive_landmarks (bool):keep disjunctive landmarks
conjunctive_landmarks (bool):keep conjunctive landmarks
no_orders (bool):discard all orderings
lm_cost_type ({NORMAL, ONE, PLUSONE}):landmark action cost adjustment
lm_rhw
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)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
reasonable_orders (bool):generate reasonable orders
only_causal_landmarks (bool):keep only causal landmarks
disjunctive_landmarks (bool):keep disjunctive landmarks
conjunctive_landmarks (bool):keep conjunctive landmarks
no_orders (bool):discard all orderings
lm_cost_type ({NORMAL, ONE, PLUSONE}):landmark action cost adjustment
lm_search
lm_search(max_depth = 10, num_tries = 10, uniform_sampling = false, cost_type = NORMAL, reasonable_orders = false, only_causal_landmarks = false, disjunctive_landmarks = true, conjunctive_landmarks = true, no_orders = false, lm_cost_type = NORMAL)
max_depth (int):max depth
num_tries (int):max number of tries
uniform_sampling (bool):uniform sampling
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
reasonable_orders (bool):generate reasonable orders
only_causal_landmarks (bool):keep only causal landmarks
disjunctive_landmarks (bool):keep disjunctive landmarks
conjunctive_landmarks (bool):keep conjunctive landmarks
no_orders (bool):discard all orderings
lm_cost_type ({NORMAL, ONE, PLUSONE}):landmark action cost adjustment
lm_zg
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)
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
reasonable_orders (bool):generate reasonable orders
only_causal_landmarks (bool):keep only causal landmarks
disjunctive_landmarks (bool):keep disjunctive landmarks
conjunctive_landmarks (bool):keep conjunctive landmarks
no_orders (bool):discard all orderings
lm_cost_type ({NORMAL, ONE, PLUSONE}):landmark action cost adjustment
synergys
lm_ff_syn
lm_ff_syn(lm_graph, admissible = false, optimal = false, alm = true, cost_type = NORMAL)
lm_graph (landmarks graph):
admissible (bool):get admissible estimate
optimal (bool):optimal cost sharing
alm (bool):use action landmarks
cost_type ({NORMAL, ONE, PLUSONE}):operator cost adjustment type
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