symbolic.Pddl

class symbolic.Pddl

Bases: pybind11_object

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: symbolic.pysymbolic.Pddl, domain: str, problem: str, apply_axioms: bool = True) -> None

   Parse the pddl specification from the domain and problem files.

   Args:

   domain: Path to the domain pddl. problem: Path to the problem pddl. apply_axioms: Whether to apply axioms to the initial state.

   Example:

   >>> import symbolic
   >>> symbolic.Pddl("../resources/domain.pddl", "../resources/problem.pddl")
   symbolic.Pddl('../resources/domain.pddl', '../resources/problem.pddl')
   

   See also

   C++: symbolic::Pddl::Pddl().

2. __init__(self: symbolic.pysymbolic.Pddl, domain: str) -> None

   Parse the pddl specification from the domain file without a problem.

   Args:

   domain: Path to the domain pddl.

   Example:

   >>> import symbolic
   >>> symbolic.Pddl("../resources/domain.pddl")
   symbolic.Pddl('../resources/domain.pddl', '')
   

   See also

   C++: symbolic::Pddl::Pddl().

Methods

__init__(*args, **kwargs)	Overloaded function.
add_object(self, name, type)
apply_actions(self, state, action)	Execute a sequence of actions from the given state
consistent_state(*args, **kwargs)	Overloaded function.
derived_state(self, state)
is_goal_satisfied(self, arg0)
is_valid(self[, verbose])	Evaluate whether the pddl specification is valid using VAL.
is_valid_action(self, state, action)	Evaluates whether the action's preconditions are satisfied.
is_valid_plan(self, arg0)
is_valid_state(*args, **kwargs)	Overloaded function.
is_valid_tuple(self, arg0, arg1, arg2)
list_valid_actions(self, arg0)
list_valid_arguments(self, arg0, arg1)
next_state(self, state, action)	Apply an action to the given state.
remove_object(self, name)

Attributes

actions
axioms
constants
derived_predicates
domain_pddl
goal
initial_state	Initial state for planning.
name	Pddl domain name.
object_map
objects
predicates
problem_pddl
state_index

add_object(self: symbolic.pysymbolic.Pddl, name: str, type: str) → None

apply_actions(self: symbolic.pysymbolic.Pddl, state: Set[str], action: List[str]) → Set[str]

Execute a sequence of actions from the given state

The action preconditions are not checked. The resulting state includes derived predicates.

Parameters:

• state – Current state.

• actions – Action calls in the form of "action(obj_a, obj_b)".

Returns:

Final state.

See also

C++: symbolic::Pddl::Execute.

consistent_state(*args, **kwargs)

Overloaded function.

1. consistent_state(self: symbolic.pysymbolic.Pddl, state: Set[str]) -> Set[str]

   Applies the axioms to the given state.

   Args:

   state: Current state.

   Returns:

   Updated state.

   See also

   C++: symbolic::Pddl::IsValidState().

2. consistent_state(self: symbolic.pysymbolic.Pddl, state_pos: Set[str], state_neg: Set[str]) -> Tuple[Set[str], Set[str]]

   Applies the axioms to the given partial state.

   Args:

   state_pos: Positive propositions in partial state. state_pos: Negative propositions in negative state.

   Returns:

   Updated state.

   See also

   C++: symbolic::Pddl::IsValidState().

derived_state(self: symbolic.pysymbolic.Pddl, state: Set[str]) → Set[str]

property initial_state

Initial state for planning.

is_goal_satisfied(self: symbolic.pysymbolic.Pddl, arg0: Set[str]) → bool

is_valid(self: symbolic.pysymbolic.Pddl, verbose: bool = False) → bool

Evaluate whether the pddl specification is valid using VAL.

Parameters:

verbose – Print diagnostic information.

Returns:

Whether the pddl specification is valid.

Example

>>> import symbolic
>>> pddl = symbolic.Pddl("../resources/domain.pddl", "../resources/problem.pddl")
>>> pddl.is_valid()
True

See also

C++: symbolic::Pddl::IsValid().

is_valid_action(self: symbolic.pysymbolic.Pddl, state: Set[str], action: str) → bool

Evaluates whether the action’s preconditions are satisfied.

Parameters:

• state – Current state.

• action – Action call in the form of "action(obj_a, obj_b)".

Returns:

Whether the action can be applied to the state.

Example

>>> import symbolic
>>> pddl = symbolic.Pddl("../resources/domain.pddl", "../resources/problem.pddl")
>>> pddl.is_valid_action(pddl.initial_state, "pick(hook)")
True
>>> pddl.is_valid_action(pddl.initial_state, "pick(box)")
False

See also

C++: symbolic::Pddl::IsValidAction().

is_valid_plan(self: symbolic.pysymbolic.Pddl, arg0: List[str]) → bool

is_valid_state(*args, **kwargs)

Overloaded function.

1. is_valid_state(self: symbolic.pysymbolic.Pddl, state: Set[str]) -> bool

   Evaluates whether the state satisfies the axioms.

   Args:

   state: Current state.

   Returns:

   Whether the state is valid.

   See also

   C++: symbolic::Pddl::IsValidState().

2. is_valid_state(self: symbolic.pysymbolic.Pddl, state_pos: Set[str], state_neg: Set[str]) -> bool

   Evaluate whether the partial state satisfies the axioms.

   Args:

   state_pos: Positive propositions in partial state. state_neg: Negative propositions in partial state.

   Returns:

   Whether the partial state is valid.

   See also

   C++: symbolic::Pddl::IsValidState().

is_valid_tuple(self: symbolic.pysymbolic.Pddl, arg0: Set[str], arg1: str, arg2: Set[str]) → bool

list_valid_actions(self: symbolic.pysymbolic.Pddl, arg0: Set[str]) → List[str]

list_valid_arguments(self: symbolic.pysymbolic.Pddl, arg0: Set[str], arg1: str) → List[List[str]]

property name

Pddl domain name.

Type:

str

next_state(self: symbolic.pysymbolic.Pddl, state: Set[str], action: str) → Set[str]

Apply an action to the given state.

The action’s preconditions are not checked. The resulting state includes derived predicates.

Parameters:

• state – Current state.

• action – Action call in the form of "action(obj_a, obj_b)".

Returns:

Next state.

Example

>>> import symbolic
>>> pddl = symbolic.Pddl("../resources/domain.pddl", "../resources/problem.pddl")
>>> next_state = pddl.next_state(pddl.initial_state, "pick(hook)")
>>> sorted(next_state)
['inhand(hook)', 'inworkspace(hook)', 'inworkspace(shelf)', 'inworkspace(table)', 'on(box, table)']

See also

C++: symbolic::Pddl::NextState().

remove_object(self: symbolic.pysymbolic.Pddl, name: str) → None