1. Basic Example
This python notebook shows the basic usage of the unified planning library.
In particular we will go through the following steps:
create a classical planning problem;
call a planner to solve the problem;
go beyond plan generation showing how to validate a plan and how to ground a planning problem;
call multiple planners in parallel.
1.1. Setup
First, we install unified_planning library and its dependencies from PyPi. Here, we use the --pre
flag to use the latest development build.
[1]:
!apt install graphviz graphviz-dev
%pip install unified-planning[pyperplan,tamer,plot]
We are now ready to use the Unified-Planning library!
1.2. Imports
The basic imports we need for this demo are abstracted in the shortcuts
package.
[2]:
import unified_planning
from unified_planning.shortcuts import *
1.3. Problem representation
In this example, we will model a very simple robot navigation problem.
1.3.1. Types
The first thing to do is to introduce a “UserType” to model the concept of a location. It is possible to introduce as many types as needed; then, for each type we will define a set of objects of that type.
In addition to UserType
s we have three built-in types: Bool
, Real
and Integer
.
[3]:
Location = UserType('Location')
1.3.2. Fluents and constants
The basic variables of a planning problem are called “fluents” and are quantities that can change over time. Fluents can have differen types, in this first example we will stick to classical “predicates” that are fluents of boolean type. Moreover, fluents can have parameters: effectively describing multiple variables.
For example, a booean fluent connected
with two parameters of type Location
(that can be interpreted as from
and to
) can be used to model a graph of locations: there exists an edge between two locations a
and b
if connected(a, b)
is true.
In this example, connected
will be a constant (i.e. it will never change in any execution), but another fluent robot_at
will be used to model where the robot is: the robot is in locatiopn l
if and only if robot_at(l)
is true (we will ensure that exactly one such l
exists, so that the robot is always in one single location).
[4]:
robot_at = unified_planning.model.Fluent('robot_at', BoolType(), l=Location)
connected = unified_planning.model.Fluent('connected', BoolType(), l_from=Location, l_to=Location)
1.3.3. Actions
Now we have the problem variables, but in order to describe the possible evolutions of a systems we need to describe how these variables can be changed and how they can evolve. We model this problem using classical, action-based planning, where a set of actions is used to characterize the possible transitions of the system from a state to another.
An action is a transition that can be applied if a specified set of preconditions is satisfied and that prescribes a set of effects that change the value of some fluents. All the fluents that are subjected to the action effects are unchanged.
We allow lifted actions, that are action with parameters: the parameters can be used to specify preconditions or effects and the planner will select among the possible values of each parameters the ones to be used to characterize a specific action.
In our example, we introduce an action called move
that has two parameters of type Location
indicating the current position of the robot l_from
and the intended destination of the movement l_to
. The move(a, b)
action is applicable only when the robot is in position a
(i.e. robot_at(a)
) and if a
and b
are connected locations (i.e. connected(a, b)
). As a result of applying the action move(a, b)
, the robot is no longer in a
and is instead in location
b
.
In the unified_planning, we can create actions by instantiating the unified_planning.InstantaneousAction
class; parameters are specified as keyword arguments to the constructor as shown below. Preconditions and effects are added by means of the add_precondition
and add_effect
methods.
[5]:
move = unified_planning.model.InstantaneousAction('move', l_from=Location, l_to=Location)
l_from = move.parameter('l_from')
l_to = move.parameter('l_to')
move.add_precondition(connected(l_from, l_to))
move.add_precondition(robot_at(l_from))
move.add_effect(robot_at(l_from), False)
move.add_effect(robot_at(l_to), True)
print(move)
action move(Location l_from, Location l_to) {
preconditions = [
connected(l_from, l_to)
robot_at(l_from)
]
effects = [
robot_at(l_from) := false
robot_at(l_to) := true
]
}
1.3.4. Creating the problem
The class that represents a planning problem is unified_planning.Problem
, it contains the set of fluents, the actions, the objects, an intial value for all the fluents and a goal to be reached by the planner. We start by adding the entities we created so far. Note that entities are not bound to one problem, we can create the actions and fluents one and create multiple problems with them.
[6]:
problem = unified_planning.model.Problem('robot')
problem.add_fluent(robot_at, default_initial_value=False)
problem.add_fluent(connected, default_initial_value=False)
problem.add_action(move)
The set of objects is a set of unified_planning.Object
instances, each represnting an element of the domain. In this example, we create NLOC
(set to 10) locations named l0
to l9
. We can create the set of objects and add it to the problem as follows.
[7]:
NLOC = 10
locations = [unified_planning.model.Object('l%s' % i, Location) for i in range(NLOC)]
problem.add_objects(locations)
Then, we need to specify the initial state. We used the default_initial_value
specification when adding the fluents, so it suffices to indicate the fluents that are initially true (this is called “closed-world assumption”. Without this specification, we would need to initialize all the possible instantiation of all the fluents).
In this example, we connect location li
with location li+1
, creating a simple “linear” graph lof locations and we set the initial position of the robot in location l0
.
[8]:
problem.set_initial_value(robot_at(locations[0]), True)
for i in range(NLOC - 1):
problem.set_initial_value(connected(locations[i], locations[i+1]), True)
Finally, we set the goal of the problem. In this example, we set ourselves to reach location l9
.
[9]:
problem.add_goal(robot_at(locations[-1]))
print(problem)
problem name = robot
types = [Location]
fluents = [
bool robot_at[l=Location]
bool connected[l_from=Location, l_to=Location]
]
actions = [
action move(Location l_from, Location l_to) {
preconditions = [
connected(l_from, l_to)
robot_at(l_from)
]
effects = [
robot_at(l_from) := false
robot_at(l_to) := true
]
}
]
objects = [
Location: [l0, l1, l2, l3, l4, l5, l6, l7, l8, l9]
]
initial fluents default = [
bool robot_at[l=Location] := false
bool connected[l_from=Location, l_to=Location] := false
]
initial values = [
robot_at(l0) := true
connected(l0, l1) := true
connected(l1, l2) := true
connected(l2, l3) := true
connected(l3, l4) := true
connected(l4, l5) := true
connected(l5, l6) := true
connected(l6, l7) := true
connected(l7, l8) := true
connected(l8, l9) := true
]
goals = [
robot_at(l9)
]
1.4. Solving Planning Problems
The most direct way to solve a planning problem is to select an available planning engine by name and use it to solve the problem. In the following we use pyperplan
to solve the problem and print the plan.
[10]:
with OneshotPlanner(name='pyperplan') as planner:
result = planner.solve(problem)
if result.status == up.engines.PlanGenerationResultStatus.SOLVED_SATISFICING:
print("Pyperplan returned: %s" % result.plan)
else:
print("No plan found.")
NOTE: To disable printing of planning engine credits, add this line to your code: `up.shortcuts.get_environment().credits_stream = None`
*** Credits ***
* In operation mode `OneshotPlanner` at line 1 of `/tmp/ipykernel_154533/1237117673.py`, you are using the following planning engine:
* Engine name: pyperplan
* Developers: Albert-Ludwigs-Universität Freiburg (Yusra Alkhazraji, Matthias Frorath, Markus Grützner, Malte Helmert, Thomas Liebetraut, Robert Mattmüller, Manuela Ortlieb, Jendrik Seipp, Tobias Springenberg, Philip Stahl, Jan Wülfing)
* Description: Pyperplan is a lightweight STRIPS planner written in Python.
Pyperplan returned: SequentialPlan:
move(l0, l1)
move(l1, l2)
move(l2, l3)
move(l3, l4)
move(l4, l5)
move(l5, l6)
move(l6, l7)
move(l7, l8)
move(l8, l9)
The unified_planning can also automatically select, among the available planners installed on the system, one that is expressive enough for the problem at hand.
[11]:
with OneshotPlanner(problem_kind=problem.kind) as planner:
result = planner.solve(problem)
print("%s returned: %s" % (planner.name, result.plan))
*** Credits ***
* In operation mode `OneshotPlanner` at line 1 of `/tmp/ipykernel_154533/3190604266.py`, you are using the following planning engine:
* Engine name: Fast Downward
* Developers: Uni Basel team and contributors (cf. https://github.com/aibasel/downward/blob/main/README.md)
* Description: Fast Downward is a domain-independent classical planning system.
Fast Downward returned: SequentialPlan:
move(l0, l1)
move(l1, l2)
move(l2, l3)
move(l3, l4)
move(l4, l5)
move(l5, l6)
move(l6, l7)
move(l7, l8)
move(l8, l9)
In this example, Pyperplan was selected. The problem.kind
property, returns an object that describes the characteristics of the problem.
[12]:
print(problem.kind.features)
{'FLAT_TYPING', 'ACTION_BASED'}
1.4.1. Beyond plan generation
OneshotPlanner
is not the only operation mode we can invoke from the unified_planning, it is just one way to interact with a planning engine. Another useful functionality is PlanValidation
that checks if a plan is valid for a problem.
[13]:
plan = result.plan
with PlanValidator(problem_kind=problem.kind, plan_kind=plan.kind) as validator:
if validator.validate(problem, plan):
print('The plan is valid')
else:
print('The plan is invalid')
*** Credits ***
* In operation mode `PlanValidator` at line 2 of `/tmp/ipykernel_154533/2705118315.py`, you are using the following planning engine:
* Engine name: Tamer
* Developers: FBK Tamer Development Team
* Description: Tamer offers the capability to generate a plan for classical, numerical and temporal problems.
* For those kind of problems tamer also offers the possibility of validating a submitted plan.
The plan is valid
It is also possible to use the Compiler
operation mode with compilation_kind=CompilationKind.GROUNDING
to create an equivalent formulation of a problem that does not use parameters for the actions.
For an in-depth tutorial about the Compiler
operation mode check the Notebook on Compilers.
[14]:
with Compiler(problem_kind=problem.kind, compilation_kind=CompilationKind.GROUNDING) as grounder:
grounding_result = grounder.compile(problem, CompilationKind.GROUNDING)
ground_problem = grounding_result.problem
print(ground_problem)
# The grounding_result can be used to "lift" a ground plan back to the level of the original problem
with OneshotPlanner(problem_kind=ground_problem.kind) as planner:
ground_plan = planner.solve(ground_problem).plan
print('Ground plan: %s' % ground_plan)
# Replace the action instances of the grounded plan with their correspoding lifted version
lifted_plan = ground_plan.replace_action_instances(grounding_result.map_back_action_instance)
print('Lifted plan: %s' % lifted_plan)
# Test the problem and plan validity
with PlanValidator(problem_kind=problem.kind, plan_kind=ground_plan.kind) as validator:
ground_validation = validator.validate(ground_problem, ground_plan)
lift_validation = validator.validate(problem, lifted_plan)
Valid = up.engines.ValidationResultStatus.VALID
assert ground_validation.status == Valid
assert lift_validation.status == Valid
*** Credits ***
* In operation mode `Compiler` at line 1 of `/tmp/ipykernel_154533/1981797297.py`, you are using the following planning engine:
* Engine name: pyperplan
* Developers: Albert-Ludwigs-Universität Freiburg (Yusra Alkhazraji, Matthias Frorath, Markus Grützner, Malte Helmert, Thomas Liebetraut, Robert Mattmüller, Manuela Ortlieb, Jendrik Seipp, Tobias Springenberg, Philip Stahl, Jan Wülfing)
* Description: Pyperplan is a lightweight STRIPS planner written in Python.
problem name = robot
types = [Location]
fluents = [
bool robot_at[l=Location]
bool connected[l_from=Location, l_to=Location]
]
actions = [
action move_l1_l2 {
preconditions = [
robot_at(l1)
]
effects = [
robot_at(l2) := true
robot_at(l1) := false
]
}
action move_l0_l1 {
preconditions = [
robot_at(l0)
]
effects = [
robot_at(l1) := true
robot_at(l0) := false
]
}
action move_l8_l9 {
preconditions = [
robot_at(l8)
]
effects = [
robot_at(l9) := true
robot_at(l8) := false
]
}
action move_l7_l8 {
preconditions = [
robot_at(l7)
]
effects = [
robot_at(l8) := true
robot_at(l7) := false
]
}
action move_l6_l7 {
preconditions = [
robot_at(l6)
]
effects = [
robot_at(l7) := true
robot_at(l6) := false
]
}
action move_l3_l4 {
preconditions = [
robot_at(l3)
]
effects = [
robot_at(l4) := true
robot_at(l3) := false
]
}
action move_l4_l5 {
preconditions = [
robot_at(l4)
]
effects = [
robot_at(l5) := true
robot_at(l4) := false
]
}
action move_l5_l6 {
preconditions = [
robot_at(l5)
]
effects = [
robot_at(l6) := true
robot_at(l5) := false
]
}
action move_l2_l3 {
preconditions = [
robot_at(l2)
]
effects = [
robot_at(l3) := true
robot_at(l2) := false
]
}
]
objects = [
Location: [l0, l1, l2, l3, l4, l5, l6, l7, l8, l9]
]
initial fluents default = [
bool robot_at[l=Location] := false
bool connected[l_from=Location, l_to=Location] := false
]
initial values = [
robot_at(l0) := true
]
goals = [
robot_at(l9)
]
*** Credits ***
* In operation mode `OneshotPlanner` at line 7 of `/tmp/ipykernel_154533/1981797297.py`, you are using the following planning engine:
* Engine name: Fast Downward
* Developers: Uni Basel team and contributors (cf. https://github.com/aibasel/downward/blob/main/README.md)
* Description: Fast Downward is a domain-independent classical planning system.
Ground plan: SequentialPlan:
move_l0_l1
move_l1_l2
move_l2_l3
move_l3_l4
move_l4_l5
move_l5_l6
move_l6_l7
move_l7_l8
move_l8_l9
Lifted plan: SequentialPlan:
move(l0, l1)
move(l1, l2)
move(l2, l3)
move(l3, l4)
move(l4, l5)
move(l5, l6)
move(l6, l7)
move(l7, l8)
move(l8, l9)
*** Credits ***
* In operation mode `PlanValidator` at line 14 of `/tmp/ipykernel_154533/1981797297.py`, you are using the following planning engine:
* Engine name: Tamer
* Developers: FBK Tamer Development Team
* Description: Tamer offers the capability to generate a plan for classical, numerical and temporal problems.
* For those kind of problems tamer also offers the possibility of validating a submitted plan.
1.4.2. Parallel planning
We can invoke different instances of a planner in parallel or different planners and return the first plan that is generated effortlessly.
[15]:
with OneshotPlanner(names=['tamer', 'tamer', 'pyperplan'],
params=[{'heuristic': 'hadd'}, {'heuristic': 'hmax'}, {}]) as planner:
plan = planner.solve(problem).plan
print("%s returned: %s" % (planner.name, plan))
*** Credits ***
* In operation mode `OneshotPlanner` at line 1 of `/tmp/ipykernel_154533/3703753692.py`, you are using a parallel planning engine with the following components:
* Engine name: Tamer
* Developers: FBK Tamer Development Team
* Description: Tamer offers the capability to generate a plan for classical, numerical and temporal problems.
* For those kind of problems tamer also offers the possibility of validating a submitted plan.
* Engine name: Tamer
* Developers: FBK Tamer Development Team
* Description: Tamer offers the capability to generate a plan for classical, numerical and temporal problems.
* For those kind of problems tamer also offers the possibility of validating a submitted plan.
* Engine name: pyperplan
* Developers: Albert-Ludwigs-Universität Freiburg (Yusra Alkhazraji, Matthias Frorath, Markus Grützner, Malte Helmert, Thomas Liebetraut, Robert Mattmüller, Manuela Ortlieb, Jendrik Seipp, Tobias Springenberg, Philip Stahl, Jan Wülfing)
* Description: Pyperplan is a lightweight STRIPS planner written in Python.
Parallel returned: SequentialPlan:
move(l0, l1)
move(l1, l2)
move(l2, l3)
move(l3, l4)
move(l4, l5)
move(l5, l6)
move(l6, l7)
move(l7, l8)
move(l8, l9)
[ ]:
from unified_planning.plot import plot_sequential_plan
Ignore the code below, it’s used to make this notebook also runnable in the Countinuous Intergation.
[ ]:
# Redefine the plot package methods imported above to print the plot to a temp file
# if the exception "could not locate runnable browser" is raised. This usually happens
# in the Continuous Integration.
from inspect import getmembers, isfunction
from unified_planning import plot
from functools import partial
import os, uuid, tempfile as tf
# Define the function that will be executed instead
def _function(original_function, *args, **kwargs):
try:
original_function(*args, **kwargs)
except Exception as e:
if "could not locate runnable browser" in str(e):
original_function(*args, **kwargs,
filename=f"{os.path.join(tf.gettempdir(), str(uuid.uuid1()))}.png"
)
else:
raise e
# Iterate over all the functions of the plot package
for function_name, function in getmembers(plot, isfunction):
# Override the original function with the new one
globals()[function_name] = partial(_function, function)
[ ]:
if plan is not None:
plot_sequential_plan(plan, figsize=(16, 4), node_size=4000, font_size=10)