The unified_planning library makes it easy to formulate planning problems and to invoke automated planners.
- Define problems in a simple, intuitive, and planner independent way
- Solve your planning problems using one of the native solvers, or by using any PDDL planner
- Dump your problems in PDDL (or ANML) format
- Parse PDDL problem formulations
- Simplification, grounding, removal of conditional effects and many other transformations are available
- and more...
from unified_planning.shortcuts import *
x = Fluent("x")
a = InstantaneousAction("a")
a.add_precondition(Not(x))
a.add_effect(x, True)
problem = Problem("basic")
problem.add_fluent(x)
problem.add_action(a)
problem.set_initial_value(x, False)
problem.add_goal(x)
with OneshotPlanner(problem_kind=problem.kind) as planner:
result = planner.solve(problem)
if result.status in unified_planning.engines.results.POSITIVE_OUTCOMES:
print(f"{planner.name} found this plan: {result.plan}")
else:
print("No plan found.")The documentation is available here
More complex examples are available as notebooks here.
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You can cite the following article:
Andrea Micheli, Arthur Bit-Monnot, Gabriele Röger, Enrico Scala, Alessandro Valentini, Luca Framba, Alberto Rovetta, Alessandro Trapasso, Luigi Bonassi, Alfonso Emilio Gerevini, Luca Iocchi, Felix Ingrand, Uwe Köckemann, Fabio Patrizi, Alessandro Saetti, Ivan Serina and Sebastian Stock.
Unified Planning: Modeling, manipulating and solving AI planning problems in Python.
SoftwareX, 2025. https://doi.org/10.1016/j.softx.2024.102012
@article{unified_planning_softwarex2025,
title = {Unified Planning: Modeling, manipulating and solving AI planning problems in Python},
author = {Andrea Micheli and Arthur Bit-Monnot and Gabriele R{\"o}ger and Enrico Scala and Alessandro Valentini and Luca Framba and Alberto Rovetta and Alessandro Trapasso and Luigi Bonassi and Alfonso Emilio Gerevini and Luca Iocchi and Felix Ingrand and Uwe Köckemann and Fabio Patrizi and Alessandro Saetti and Ivan Serina and Sebastian Stock},
journal = {SoftwareX},
volume = {29},
pages = {102012},
year = {2025},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2024.102012},
url = {https://www.sciencedirect.com/science/article/pii/S2352711024003820},
abstract = {Automated planning is a branch of artificial intelligence aiming at finding a course of action that achieves specified goals, given a description of the initial state of a system and a model of possible actions. There are plenty of planning approaches working under different assumptions and with different features (e.g. classical, temporal, and numeric planning). When automated planning is used in practice, however, the set of required features is often initially unclear. The Unified Planning (UP) library addresses this issue by providing a feature-rich Python API for modeling automated planning problems, which are solved seamlessly by planning engines that specify the set of features they support. Once a problem is modeled, UP can automatically find engines that can solve it, based on the features used in the model. This greatly reduces the commitment to specific planning approaches and bridges the gap between planning technology and its users.}
}
This library is being developed for the AIPlan4EU H2020 project (https://aiplan4eu-project.eu) that is funded by the European Commission under grant agreement number 101016442.
