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

Definitions of properties in the descriptions below:

• admissible: h(s) <= h*(s) for all states s

• consistent: h(s) + c(s, s') >= h(s') for all states s connected to states s' by an action with cost c(s, s')

• safe: h(s) = infinity is only true for states with h*(s) = infinity

• preferred operators: this heuristic identifies preferred operators

Additive heuristic

add(cost_type=NORMAL)

• 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.

Language features supported:

• action costs: supported

• conditional_effects: supported

• axioms: supported (in the sense that the planner won't complain -- handling of axioms might be very stupid and even render the heuristic unsafe)

Properties:

• admissible: no

• consistent: no

• safe: yes for tasks without axioms

• preferred operators: yes

Blind heuristic

Returns cost of cheapest action for non-goal states, 0 for goal states

blind(cost_type=NORMAL)

• 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.

Language features supported:

• action costs: supported

• conditional_effects: supported

• axioms: supported

Properties:

• admissible: yes

• consistent: yes

• safe: yes

• preferred operators: no

Context-enhanced additive heuristic

cea(cost_type=NORMAL)

• 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.

Language features supported:

• action costs: supported

• conditional_effects: supported

• axioms: supported (in the sense that the planner won't complain -- handling of axioms might be very stupid and even render the heuristic unsafe)

Properties:

• admissible: no

• consistent: no

• safe: no

• preferred operators: yes

Causal graph heuristic

cg(cost_type=NORMAL)

• 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.

Language features supported:

• action costs: supported

• conditional_effects: supported

• axioms: supported (in the sense that the planner won't complain -- handling of axioms might be very stupid and even render the heuristic unsafe)

Properties:

• admissible: no

• consistent: no

• safe: no

• preferred operators: yes

Canonical PDB

cpdbs(cost_type=NORMAL, patterns=None, combo=false, max_states=1000000)

• 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.

• 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): maximum abstraction size for combo strategy

FF heuristic

See also Synergy.

ff(cost_type=NORMAL)

• 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.

Language features supported:

• action costs: supported

• conditional_effects: supported

• axioms: supported (in the sense that the planner won't complain -- handling of axioms might be very stupid and even render the heuristic unsafe)

Properties:

• admissible: no

• consistent: no

• safe: yes for tasks without axioms

• preferred operators: yes

Genetic Algorithm PDB

gapdb(pdb_max_size=50000, num_collections=5, num_episodes=30, mutation_probability=0.01, disjoint=false, cost_type=NORMAL)

• pdb_max_size (int): max number of states per pdb

• num_collections (int): number of pattern collections to maintain

• num_episodes (int): number of episodes

• mutation_probability (double): probability between 0 and 1 for flipping a bit

• disjoint (bool): using disjoint variables in the patterns of a collection

• 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.

Goal count heuristic

goalcount(cost_type=NORMAL)

• 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.

Language features supported:

• action costs: ignored by design

• conditional_effects: supported

• axioms: supported

Properties:

• admissible: no

• consistent: no

• safe: yes

• preferred operators: no

h^m heuristic

hm(m=2, cost_type=NORMAL)

• m (int): subset size

• 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.

Language features supported:

• action costs: supported

• conditional_effects: ignored

• axioms: ignored

Properties:

• admissible: yes for tasks without conditional effects or axioms

• consistent: yes for tasks without conditional effects or axioms

• safe: yes for tasks without conditional effects or axioms

• preferred operators: no

Max heuristic

hmax(cost_type=NORMAL)

• 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.

Language features supported:

• action costs: supported

• conditional_effects: supported

• axioms: supported (in the sense that the planner won't complain -- handling of axioms might be very stupid and even render the heuristic unsafe)

Properties:

• admissible: yes for tasks without axioms

• consistent: yes for tasks without axioms

• safe: yes for tasks without axioms

• preferred operators: no

iPDB

the pattern selection procedure by Haslum et al. (AAAI 2007); see also Sievers et al. (SoCS 2012) for implementation notes

ipdb(pdb_max_size=2000000, collection_max_size=20000000, num_samples=1000, min_improvement=10, cost_type=NORMAL)

• pdb_max_size (int): max number of states per pdb

• collection_max_size (int): max number of states for collection

• num_samples (int): number of samples

• min_improvement (int): minimum improvement while hill climbing

• 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.

Landmark-count heuristic

See also Synergy

lmcount(lm_graph, admissible=false, optimal=false, pref=false, alm=true, cost_type=NORMAL)

• lm_graph (LandmarkGraph): the set of landmarks to use for this heuristic. The set of landmarks can be specified here, or predefined (see LandmarkGraph).

• admissible (bool): get admissible estimate

• optimal (bool): use optimal (LP-based) cost sharing (only makes sense with admissible=true)

• pref (bool): identify preferred operators (see Using preferred operators with the lmcount heuristic)

• alm (bool): use action landmarks

• 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.

Note: to use optimal=true, you must build the planner with USE_LP=1. See LPBuildInstructions.

Optimal search: when using landmarks for optimal search (admissible=true), you probably also want to enable the mpd option of the A* algorithm to improve heuristic estimates

cost_type parameter: only used when admissible=true (see LandmarkGraph)

Language features supported:

• action costs: supported

• conditional_effects: supported if admissible=false

• axioms: supported if admissible=false (but may behave stupidly and lead to an unsafe heuristic)

Properties:

• admissible: yes if admissible=true and there are neither conditional effects nor axioms

• consistent: no

• safe: yes (except maybe on tasks with axioms or when using admissible=true on tasks with conditional effects)

• preferred operators: yes (if enabled; see pref option)

Landmark-cut heuristic

lmcut(cost_type=NORMAL)

• 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.

Language features supported:

• action costs: supported

• conditional_effects: not supported

• axioms: not supported

Properties:

• admissible: yes

• consistent: no

• safe: yes

• preferred operators: no

Merge-and-shrink heuristic

Note: The parameter space and syntax for the merge-and-shrink heuristic has changed significantly in August 2011.

merge_and_shrink(merge_strategy=MERGE_LINEAR_CG_GOAL_LEVEL, shrink_strategy=shrink_fh(max_states=50000, max_states_before_merge=50000, shrink_f=high, shrink_h=low), reduce_labels=true, expensive_statistics=false, cost_type=NORMAL)

• 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 strategy

• shrink_strategy (ShrinkStrategy): shrink strategy; try one of the following:

shrink_fh(max_states=N)

• f-preserving abstractions from the Helmert/Haslum/Hoffmann ICAPS 2007 paper (called HHH in the IJCAI 2011 paper by Nissim, Hoffmann and Helmert). Here, N is a numerical parameter for which sensible values include 1000, 10000, 50000, 100000 and 200000. Combine this with the default merge strategy MERGE_LINEAR_CG_GOAL_LEVEL to match the heuristic in the paper.

shrink_bisimulation(max_states=infinity, threshold=1, greedy=true, initialize_by_h=false, group_by_h=false)

• Greedy bisimulation without size bound (called M&S-gop in the IJCAI 2011 paper by Nissim, Hoffmann and Helmert). Combine this with the merge strategy MERGE_LINEAR_REVERSE_LEVEL to match the heuristic in the paper.

shrink_bisimulation(max_states=N, greedy=false, initialize_by_h=true, group_by_h=true)

• Exact bisimulation with a size limit (called DFP-bop in the IJCAI 2011 paper by Nissim, Hoffmann and Helmert), where N is a numerical parameter for which sensible values include 1000, 10000, 50000, 100000 and 200000. Combine this with the merge strategy MERGE_LINEAR_REVERSE_LEVEL to match the heuristic in the paper.

• reduce_labels (bool): enable label reduction. Note: it is hard to fathom a scenario where label reduction is a bad idea. The overhead should be low and the gains in time and memory can be massive. So unless you really know what you're doing, don't set this to false. (The point of this option is to perform controlled experiments on how useful label reduction is exactly.)

• expensive_statistics (bool): show statistics on "unique unlabeled edges" (WARNING: these are *very* slow, i.e. too expensive to show by default (in terms of time and memory). When this is used, the planner prints a big warning on stderr with information on the performance impact. Don't use when benchmarking!)

• 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.

Language features supported:

• action costs: supported

• conditional_effects: not supported

• axioms: not supported

Properties:

• admissible: yes

• consistent: yes

• safe: yes

• preferred operators: no

Pattern database heuristic

TODO

pdb(cost_type=NORMAL, max_states=1000000, pattern=None)

• 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.

• max_states (int): maximal number of abstract states in the pattern database

• pattern (list of int): list of variable numbers of the planning task that should be used as pattern. Default: the variables are selected automatically based on a simple greedy strategy.

Zero-One PDB

zopdbs(cost_type=NORMAL, patterns=None, combo=false, max_states=1000000)

• 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.

• 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): maximum abstraction size for combo strategy

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