concepts.pdsketch.strips.strips_expression.SStateDict#

class SStateDict[source]#

Bases: dict, Dict[str, Set[Tuple[int | str, …]]]

Methods

add(predicate_name, arguments)

as_state()

clear()

clone()

contains(predicate_name, arguments[, ...])

Check whether the state contains the given proposition.

copy()

fromkeys([value])

Create a new dictionary with keys from iterable and values set to value.

get(key[, default])

Return the value for key if key is in the dictionary, else default.

items()

keys()

pop(k[,d])

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()

Remove and return a (key, value) pair as a 2-tuple.

remove(predicate_name, arguments)

setdefault(key[, default])

Insert key with a value of default if key is not in the dictionary.

update([E, ]**F)

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()

add(predicate_name, arguments)[source]#
Parameters:
as_state()[source]#
Return type:

SState

clone()[source]#
contains(predicate_name, arguments, negated=False, check_negation=False)[source]#

Check whether the state contains the given proposition.

Parameters:

  • predicate_name (str) – the name of the predicate.

  • arguments (Sequence[int | str]) – the arguments of the predicate, as a tuple of integers or strings.

  • negated (bool) – whether the proposition is negated. If True, the function will check whether the state does not contain the proposition.

  • check_negation (bool) – whether the function should also check “{predicate_name}_not” in the state. This will only be used when negated is True. This is useful for delete-relaxed planning.

Returns:

True if the state contains the proposition, False otherwise.

Return type:

bool

remove(predicate_name, arguments)[source]#
Parameters:
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