27590
Comment:
|
27578
|
Deletions are marked like this. | Additions are marked like this. |
Line 230: | Line 230: |
would perform the preprocessing phase of the merge and shrink heuristic 5 times (once before each iteration). | would perform the preprocessing phase of the ipdb heuristic 5 times (once before each iteration). |
Contents
A* search (eager)
A* is a special case of eager best first search that uses g+h as f-function. We break ties using the evaluator. Closed nodes are re-opened.
astar(eval, lazy_evaluator=<none>, pruning=null(), cost_type=normal, bound=infinity, max_time=infinity, verbosity=normal)
eval (Evaluator): evaluator for h-value
lazy_evaluator (Evaluator): An evaluator that re-evaluates a state before it is expanded.
pruning (PruningMethod): Pruning methods can prune or reorder the set of applicable operators in each state and thereby influence the number and order of successor states that are considered.
cost_type ({normal, one, plusone}): Operator cost adjustment type. No matter what this setting is, axioms will always be considered as actions of cost 0 by the heuristics that treat axioms as actions.
normal: all actions are accounted for with their real cost
one: all actions are accounted for as unit cost
plusone: all actions are accounted for as their real cost + 1 (except if all actions have original cost 1, in which case cost 1 is used). This is the behaviour known for the heuristics of the LAMA planner. This is intended to be used by the heuristics, not search engines, but is supported for both.
bound (int): exclusive depth bound on g-values. Cutoffs are always performed according to the real cost, regardless of the cost_type parameter
max_time (double): maximum time in seconds the search is allowed to run for. The timeout is only checked after each complete search step (usually a node expansion), so the actual runtime can be arbitrarily longer. Therefore, this parameter should not be used for time-limiting experiments. Timed-out searches are treated as failed searches, just like incomplete search algorithms that exhaust their search space.
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
lazy_evaluator: When a state s is taken out of the open list, the lazy evaluator h re-evaluates s. If h(s) changes (for example because h is path-dependent), s is not expanded, but instead reinserted into the open list. This option is currently only present for the A* algorithm.
Equivalent statements using general eager search
--search astar(evaluator)
is equivalent to
--evaluator h=evaluator --search eager(tiebreaking([sum([g(), h]), h], unsafe_pruning=false), reopen_closed=true, f_eval=sum([g(), h]))
Eager best-first search
eager(open, reopen_closed=false, f_eval=<none>, preferred=[], pruning=null(), cost_type=normal, bound=infinity, max_time=infinity, verbosity=normal)
open (OpenList): open list
reopen_closed (bool): reopen closed nodes
f_eval (Evaluator): set evaluator for jump statistics. (Optional; if no evaluator is used, jump statistics will not be displayed.)
preferred (list of Evaluator): use preferred operators of these evaluators
pruning (PruningMethod): Pruning methods can prune or reorder the set of applicable operators in each state and thereby influence the number and order of successor states that are considered.
cost_type ({normal, one, plusone}): Operator cost adjustment type. No matter what this setting is, axioms will always be considered as actions of cost 0 by the heuristics that treat axioms as actions.
normal: all actions are accounted for with their real cost
one: all actions are accounted for as unit cost
plusone: all actions are accounted for as their real cost + 1 (except if all actions have original cost 1, in which case cost 1 is used). This is the behaviour known for the heuristics of the LAMA planner. This is intended to be used by the heuristics, not search engines, but is supported for both.
bound (int): exclusive depth bound on g-values. Cutoffs are always performed according to the real cost, regardless of the cost_type parameter
max_time (double): maximum time in seconds the search is allowed to run for. The timeout is only checked after each complete search step (usually a node expansion), so the actual runtime can be arbitrarily longer. Therefore, this parameter should not be used for time-limiting experiments. Timed-out searches are treated as failed searches, just like incomplete search algorithms that exhaust their search space.
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
Greedy search (eager)
eager_greedy(evals, preferred=[], boost=0, pruning=null(), cost_type=normal, bound=infinity, max_time=infinity, verbosity=normal)
evals (list of Evaluator): evaluators
preferred (list of Evaluator): use preferred operators of these evaluators
boost (int): boost value for preferred operator open lists
pruning (PruningMethod): Pruning methods can prune or reorder the set of applicable operators in each state and thereby influence the number and order of successor states that are considered.
cost_type ({normal, one, plusone}): Operator cost adjustment type. No matter what this setting is, axioms will always be considered as actions of cost 0 by the heuristics that treat axioms as actions.
normal: all actions are accounted for with their real cost
one: all actions are accounted for as unit cost
plusone: all actions are accounted for as their real cost + 1 (except if all actions have original cost 1, in which case cost 1 is used). This is the behaviour known for the heuristics of the LAMA planner. This is intended to be used by the heuristics, not search engines, but is supported for both.
bound (int): exclusive depth bound on g-values. Cutoffs are always performed according to the real cost, regardless of the cost_type parameter
max_time (double): maximum time in seconds the search is allowed to run for. The timeout is only checked after each complete search step (usually a node expansion), so the actual runtime can be arbitrarily longer. Therefore, this parameter should not be used for time-limiting experiments. Timed-out searches are treated as failed searches, just like incomplete search algorithms that exhaust their search space.
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
Open list: In most cases, eager greedy best first search uses an alternation open list with one queue for each evaluator. If preferred operator evaluators are used, it adds an extra queue for each of these evaluators that includes only the nodes that are generated with a preferred operator. If only one evaluator and no preferred operator evaluator is used, the search does not use an alternation open list but a standard open list with only one queue.
Closed nodes: Closed node are not re-opened
Equivalent statements using general eager search
--evaluator h2=eval2 --search eager_greedy([eval1, h2], preferred=h2, boost=100)
is equivalent to
--evaluator h1=eval1 --heuristic h2=eval2 --search eager(alt([single(h1), single(h1, pref_only=true), single(h2), single(h2, pref_only=true)], boost=100), preferred=h2)
--search eager_greedy([eval1, eval2])
is equivalent to
--search eager(alt([single(eval1), single(eval2)]))
--evaluator h1=eval1 --search eager_greedy(h1, preferred=h1)
is equivalent to
--evaluator h1=eval1 --search eager(alt([single(h1), single(h1, pref_only=true)]), preferred=h1)
--search eager_greedy(eval1)
is equivalent to
--search eager(single(eval1))
Eager weighted A* search
eager_wastar(evals, preferred=[], reopen_closed=true, boost=0, w=1, pruning=null(), cost_type=normal, bound=infinity, max_time=infinity, verbosity=normal)
evals (list of Evaluator): evaluators
preferred (list of Evaluator): use preferred operators of these evaluators
reopen_closed (bool): reopen closed nodes
boost (int): boost value for preferred operator open lists
w (int): evaluator weight
pruning (PruningMethod): Pruning methods can prune or reorder the set of applicable operators in each state and thereby influence the number and order of successor states that are considered.
cost_type ({normal, one, plusone}): Operator cost adjustment type. No matter what this setting is, axioms will always be considered as actions of cost 0 by the heuristics that treat axioms as actions.
normal: all actions are accounted for with their real cost
one: all actions are accounted for as unit cost
plusone: all actions are accounted for as their real cost + 1 (except if all actions have original cost 1, in which case cost 1 is used). This is the behaviour known for the heuristics of the LAMA planner. This is intended to be used by the heuristics, not search engines, but is supported for both.
bound (int): exclusive depth bound on g-values. Cutoffs are always performed according to the real cost, regardless of the cost_type parameter
max_time (double): maximum time in seconds the search is allowed to run for. The timeout is only checked after each complete search step (usually a node expansion), so the actual runtime can be arbitrarily longer. Therefore, this parameter should not be used for time-limiting experiments. Timed-out searches are treated as failed searches, just like incomplete search algorithms that exhaust their search space.
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
Open lists and equivalent statements using general eager search: See corresponding notes for "(Weighted) A* search (lazy)"
Note: Eager weighted A* search uses an alternation open list while A* search uses a tie-breaking open list. Consequently,
--search eager_wastar([h()], w=1)
is not equivalent to
--search astar(h())
Lazy enforced hill-climbing
ehc(h, preferred_usage=prune_by_preferred, preferred=[], cost_type=normal, bound=infinity, max_time=infinity, verbosity=normal)
h (Evaluator): heuristic
preferred_usage ({prune_by_preferred, rank_preferred_first}): preferred operator usage
prune_by_preferred: prune successors achieved by non-preferred operators
rank_preferred_first: first insert successors achieved by preferred operators, then those by non-preferred operators
preferred (list of Evaluator): use preferred operators of these evaluators
cost_type ({normal, one, plusone}): Operator cost adjustment type. No matter what this setting is, axioms will always be considered as actions of cost 0 by the heuristics that treat axioms as actions.
normal: all actions are accounted for with their real cost
one: all actions are accounted for as unit cost
plusone: all actions are accounted for as their real cost + 1 (except if all actions have original cost 1, in which case cost 1 is used). This is the behaviour known for the heuristics of the LAMA planner. This is intended to be used by the heuristics, not search engines, but is supported for both.
bound (int): exclusive depth bound on g-values. Cutoffs are always performed according to the real cost, regardless of the cost_type parameter
max_time (double): maximum time in seconds the search is allowed to run for. The timeout is only checked after each complete search step (usually a node expansion), so the actual runtime can be arbitrarily longer. Therefore, this parameter should not be used for time-limiting experiments. Timed-out searches are treated as failed searches, just like incomplete search algorithms that exhaust their search space.
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
Iterated search
iterated(engine_configs, pass_bound=true, repeat_last=false, continue_on_fail=false, continue_on_solve=true, cost_type=normal, bound=infinity, max_time=infinity, verbosity=normal)
engine_configs (list of SearchEngine): list of search engines for each phase
pass_bound (bool): use bound from previous search. The bound is the real cost of the plan found before, regardless of the cost_type parameter.
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
cost_type ({normal, one, plusone}): Operator cost adjustment type. No matter what this setting is, axioms will always be considered as actions of cost 0 by the heuristics that treat axioms as actions.
normal: all actions are accounted for with their real cost
one: all actions are accounted for as unit cost
plusone: all actions are accounted for as their real cost + 1 (except if all actions have original cost 1, in which case cost 1 is used). This is the behaviour known for the heuristics of the LAMA planner. This is intended to be used by the heuristics, not search engines, but is supported for both.
bound (int): exclusive depth bound on g-values. Cutoffs are always performed according to the real cost, regardless of the cost_type parameter
max_time (double): maximum time in seconds the search is allowed to run for. The timeout is only checked after each complete search step (usually a node expansion), so the actual runtime can be arbitrarily longer. Therefore, this parameter should not be used for time-limiting experiments. Timed-out searches are treated as failed searches, just like incomplete search algorithms that exhaust their search space.
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
Note 1: We don't cache heuristic values between search iterations at the moment. If you perform a LAMA-style iterative search, heuristic values will be computed multiple times.
Note 2: The configuration
--search "iterated([lazy_wastar(ipdb(),w=10), lazy_wastar(ipdb(),w=5), lazy_wastar(ipdb(),w=3), lazy_wastar(ipdb(),w=2), lazy_wastar(ipdb(),w=1)])"
would perform the preprocessing phase of the ipdb heuristic 5 times (once before each iteration).
To avoid this, use heuristic predefinition, which avoids duplicate preprocessing, as follows:
--evaluator "h=ipdb()" --search "iterated([lazy_wastar(h,w=10), lazy_wastar(h,w=5), lazy_wastar(h,w=3), lazy_wastar(h,w=2), lazy_wastar(h,w=1)])"
Note 3: If you reuse the same landmark count heuristic (using heuristic predefinition) between iterations, the path data (that is, landmark status for each visited state) will be saved between iterations.
Lazy best-first search
lazy(open, reopen_closed=false, preferred=[], randomize_successors=false, preferred_successors_first=false, random_seed=-1, cost_type=normal, bound=infinity, max_time=infinity, verbosity=normal)
open (OpenList): open list
reopen_closed (bool): reopen closed nodes
preferred (list of Evaluator): use preferred operators of these evaluators
randomize_successors (bool): randomize the order in which successors are generated
preferred_successors_first (bool): consider preferred operators first
random_seed (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.
cost_type ({normal, one, plusone}): Operator cost adjustment type. No matter what this setting is, axioms will always be considered as actions of cost 0 by the heuristics that treat axioms as actions.
normal: all actions are accounted for with their real cost
one: all actions are accounted for as unit cost
plusone: all actions are accounted for as their real cost + 1 (except if all actions have original cost 1, in which case cost 1 is used). This is the behaviour known for the heuristics of the LAMA planner. This is intended to be used by the heuristics, not search engines, but is supported for both.
bound (int): exclusive depth bound on g-values. Cutoffs are always performed according to the real cost, regardless of the cost_type parameter
max_time (double): maximum time in seconds the search is allowed to run for. The timeout is only checked after each complete search step (usually a node expansion), so the actual runtime can be arbitrarily longer. Therefore, this parameter should not be used for time-limiting experiments. Timed-out searches are treated as failed searches, just like incomplete search algorithms that exhaust their search space.
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
Successor ordering: When using randomize_successors=true and preferred_successors_first=true, randomization happens before preferred operators are moved to the front.
Greedy search (lazy)
lazy_greedy(evals, preferred=[], reopen_closed=false, boost=1000, randomize_successors=false, preferred_successors_first=false, random_seed=-1, cost_type=normal, bound=infinity, max_time=infinity, verbosity=normal)
evals (list of Evaluator): evaluators
preferred (list of Evaluator): use preferred operators of these evaluators
reopen_closed (bool): reopen closed nodes
boost (int): boost value for alternation queues that are restricted to preferred operator nodes
randomize_successors (bool): randomize the order in which successors are generated
preferred_successors_first (bool): consider preferred operators first
random_seed (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.
cost_type ({normal, one, plusone}): Operator cost adjustment type. No matter what this setting is, axioms will always be considered as actions of cost 0 by the heuristics that treat axioms as actions.
normal: all actions are accounted for with their real cost
one: all actions are accounted for as unit cost
plusone: all actions are accounted for as their real cost + 1 (except if all actions have original cost 1, in which case cost 1 is used). This is the behaviour known for the heuristics of the LAMA planner. This is intended to be used by the heuristics, not search engines, but is supported for both.
bound (int): exclusive depth bound on g-values. Cutoffs are always performed according to the real cost, regardless of the cost_type parameter
max_time (double): maximum time in seconds the search is allowed to run for. The timeout is only checked after each complete search step (usually a node expansion), so the actual runtime can be arbitrarily longer. Therefore, this parameter should not be used for time-limiting experiments. Timed-out searches are treated as failed searches, just like incomplete search algorithms that exhaust their search space.
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
Successor ordering: When using randomize_successors=true and preferred_successors_first=true, randomization happens before preferred operators are moved to the front.
Open lists: In most cases, lazy greedy best first search uses an alternation open list with one queue for each evaluator. If preferred operator evaluators are used, it adds an extra queue for each of these evaluators that includes only the nodes that are generated with a preferred operator. If only one evaluator and no preferred operator evaluator is used, the search does not use an alternation open list but a standard open list with only one queue.
Equivalent statements using general lazy search
--evaluator h2=eval2 --search lazy_greedy([eval1, h2], preferred=h2, boost=100)
is equivalent to
--evaluator h1=eval1 --heuristic h2=eval2 --search lazy(alt([single(h1), single(h1, pref_only=true), single(h2), single(h2, pref_only=true)], boost=100), preferred=h2)
--search lazy_greedy([eval1, eval2], boost=100)
is equivalent to
--search lazy(alt([single(eval1), single(eval2)], boost=100))
--evaluator h1=eval1 --search lazy_greedy(h1, preferred=h1)
is equivalent to
--evaluator h1=eval1 --search lazy(alt([single(h1), single(h1, pref_only=true)], boost=1000), preferred=h1)
--search lazy_greedy(eval1)
is equivalent to
--search lazy(single(eval1))
(Weighted) A* search (lazy)
Weighted A* is a special case of lazy best first search.
lazy_wastar(evals, preferred=[], reopen_closed=true, boost=1000, w=1, randomize_successors=false, preferred_successors_first=false, random_seed=-1, cost_type=normal, bound=infinity, max_time=infinity, verbosity=normal)
evals (list of Evaluator): evaluators
preferred (list of Evaluator): use preferred operators of these evaluators
reopen_closed (bool): reopen closed nodes
boost (int): boost value for preferred operator open lists
w (int): evaluator weight
randomize_successors (bool): randomize the order in which successors are generated
preferred_successors_first (bool): consider preferred operators first
random_seed (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.
cost_type ({normal, one, plusone}): Operator cost adjustment type. No matter what this setting is, axioms will always be considered as actions of cost 0 by the heuristics that treat axioms as actions.
normal: all actions are accounted for with their real cost
one: all actions are accounted for as unit cost
plusone: all actions are accounted for as their real cost + 1 (except if all actions have original cost 1, in which case cost 1 is used). This is the behaviour known for the heuristics of the LAMA planner. This is intended to be used by the heuristics, not search engines, but is supported for both.
bound (int): exclusive depth bound on g-values. Cutoffs are always performed according to the real cost, regardless of the cost_type parameter
max_time (double): maximum time in seconds the search is allowed to run for. The timeout is only checked after each complete search step (usually a node expansion), so the actual runtime can be arbitrarily longer. Therefore, this parameter should not be used for time-limiting experiments. Timed-out searches are treated as failed searches, just like incomplete search algorithms that exhaust their search space.
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
Successor ordering: When using randomize_successors=true and preferred_successors_first=true, randomization happens before preferred operators are moved to the front.
Open lists: In the general case, it uses an alternation open list with one queue for each evaluator h that ranks the nodes by g + w * h. If preferred operator evaluators are used, it adds for each of the evaluators another such queue that only inserts nodes that are generated by preferred operators. In the special case with only one evaluator and no preferred operator evaluators, it uses a single queue that is ranked by g + w * h.
Equivalent statements using general lazy search
--evaluator h1=eval1 --search lazy_wastar([h1, eval2], w=2, preferred=h1, bound=100, boost=500)
is equivalent to
--evaluator h1=eval1 --heuristic h2=eval2 --search lazy(alt([single(sum([g(), weight(h1, 2)])), single(sum([g(), weight(h1, 2)]), pref_only=true), single(sum([g(), weight(h2, 2)])), single(sum([g(), weight(h2, 2)]), pref_only=true)], boost=500), preferred=h1, reopen_closed=true, bound=100)
--search lazy_wastar([eval1, eval2], w=2, bound=100)
is equivalent to
--search lazy(alt([single(sum([g(), weight(eval1, 2)])), single(sum([g(), weight(eval2, 2)]))], boost=1000), reopen_closed=true, bound=100)
--search lazy_wastar([eval1, eval2], bound=100, boost=0)
is equivalent to
--search lazy(alt([single(sum([g(), eval1])), single(sum([g(), eval2]))]) reopen_closed=true, bound=100)
--search lazy_wastar(eval1, w=2)
is equivalent to
--search lazy(single(sum([g(), weight(eval1, 2)])), reopen_closed=true)