<h1 align="center">
<b>pddl</b>
</h1>
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`pddl` aims to be an unquestionable and complete parser for PDDL 3.1.
## Install
- from PyPI:
```
pip install pddl
```
- from source (`main` branch):
```
pip install git+https://github.com/AI-Planning/pddl.git
```
- or, clone the repository and install:
```
git clone https://github.com/AI-Planning/pddl.git
cd pddl
pip install .
```
## Quickstart
You can use the `pddl` package in two ways: as a library, and as a CLI tool.
### As a library
This is an example of how you can build a PDDL domain or problem
programmatically:
```python
from pddl.logic import Predicate, constants, variables
from pddl.core import Domain, Problem
from pddl.action import Action
from pddl.formatter import domain_to_string, problem_to_string
from pddl.requirements import Requirements
# set up variables and constants
x, y, z = variables("x y z", types=["type_1"])
a, b, c = constants("a b c", type_="type_1")
# define predicates
p1 = Predicate("p1", x, y, z)
p2 = Predicate("p2", x, y)
# define actions
a1 = Action(
"action-1",
parameters=[x, y, z],
precondition=p1(x, y, z) & ~p2(y, z),
effect=p2(y, z)
)
# define the domain object.
requirements = [Requirements.STRIPS, Requirements.TYPING]
domain = Domain("my_domain",
requirements=requirements,
types={"type_1": None},
constants=[a, b, c],
predicates=[p1, p2],
actions=[a1])
print(domain_to_string(domain))
```
that gives:
```output
(define (domain my_domain)
(:requirements :strips :typing)
(:types type_1)
(:constants a b c - type_1)
(:predicates (p1 ?x - type_1 ?y - type_1 ?z - type_1) (p2 ?x - type_1 ?y - type_1))
(:action action-1
:parameters (?x - type_1 ?y - type_1 ?z - type_1)
:precondition (and (p1 ?x ?y ?z) (not (p2 ?y ?z)))
:effect (p2 ?y ?z)
)
)
```
As well as a PDDL problem:
```python
problem = Problem(
"problem-1",
domain=domain,
requirements=requirements,
objects=[a, b, c],
init=[p1(a, b, c), ~p2(b, c)],
goal=p2(b, c)
)
print(problem_to_string(problem))
```
Output:
```output
(define (problem problem-1)
(:domain my_domain)
(:requirements :strips :typing)
(:objects a b c - type_1)
(:init (not (p2 b c)) (p1 a b c))
(:goal (p2 b c))
)
```
Example parsing:
```python
from pddl import parse_domain, parse_problem
domain = parse_domain('d.pddl')
problem = parse_problem('p.pddl')
```
### As CLI tool
The package can also be used as a CLI tool.
Supported commands are:
- `pddl domain FILE`: validate a PDDL domain file, and print it formatted.
- `pddl problem FILE`: validate a PDDL problem file, and print it formatted.
## Features
Supported [PDDL 3.1](https://helios.hud.ac.uk/scommv/IPC-14/repository/kovacs-pddl-3.1-2011.pdf)
requirements:
- [x] `:strips`
- [x] `:typing`
- [x] `:negative-preconditions`
- [x] `:disjunctive-preconditions`
- [x] `:equality`
- [x] `:existential-preconditions`
- [x] `:universal-preconditions`
- [x] `:quantified-preconditions`
- [x] `:conditional-effects`
- [ ] `:fluents`
- [x] `:numeric-fluents`
- [x] `:non-deterministic` (see [6th IPC: Uncertainty Part](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.163.7140&rep=rep1&type=pdf))
- [x] `:adl`
- [ ] `:durative-actions`
- [ ] `:duration-inequalities`
- [x] `:derived-predicates`
- [ ] `:timed-initial-literals`
- [ ] `:preferences`
- [ ] `:constraints`
- [x] `:action-costs`
## Development
If you want to contribute, here's how to set up your development environment.
- Install [Pipenv](https://pipenv-fork.readthedocs.io/en/latest/)
- Clone the repository: `git clone https://github.com/AI-Planning/pddl.git && cd pddl`
- Install development dependencies: `pipenv shell --python 3.8 && pipenv install --dev`
## Tests
To run tests: `tox`
To run only the code tests: `tox -e py37`
To run only the code style checks: `tox -e flake8`
## Docs
To build the docs: `mkdocs build`
To view documentation in a browser: `mkdocs serve`
and then go to [http://localhost:8000](http://localhost:8000)
## Authors
- [Marco Favorito](https://marcofavorito.me)
- [Francesco Fuggitti](https://francescofuggitti.github.io)
- [Christian Muise](http://www.haz.ca/)
## License
`pddl` is released under the MIT License.
Copyright (c) 2021-2022 WhiteMech
## Acknowledgements
The `pddl` project is partially supported by the ERC Advanced Grant WhiteMech
(No. 834228), the EU ICT-48 2020 project TAILOR (No. 952215),
the PRIN project RIPER (No. 20203FFYLK), and the JPMorgan AI Faculty
Research Award "Resilience-based Generalized Planning and Strategic
Reasoning".
# Change Log
## 0.1.0 (2021-06-21)
The first official release of pddl.
Main features:
* Specify PDDL domains and problems programmatically.
* Parsing for PDDL domains and problems.
## 0.0.1 (2020-07-30)
* First commit on the package.
Raw data
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"description": "<h1 align=\"center\">\n <b>pddl</b>\n</h1>\n\n<p align=\"center\">\n <a href=\"https://pypi.org/project/pddl\">\n <img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/pddl\">\n </a>\n <a href=\"https://pypi.org/project/pddl\">\n <img alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/pddl\" />\n </a>\n <a href=\"\">\n <img alt=\"PyPI - Status\" src=\"https://img.shields.io/pypi/status/pddl\" />\n </a>\n <a href=\"\">\n <img alt=\"PyPI - Implementation\" src=\"https://img.shields.io/pypi/implementation/pddl\">\n </a>\n <a href=\"\">\n <img alt=\"PyPI - Wheel\" src=\"https://img.shields.io/pypi/wheel/pddl\">\n </a>\n <a href=\"https://github.com/AI-Planning/pddl/blob/main/LICENSE\">\n <img alt=\"GitHub\" src=\"https://img.shields.io/github/license/AI-Planning/pddl\">\n </a>\n</p>\n<p align=\"center\">\n <a href=\"\">\n <img alt=\"test\" src=\"https://github.com/AI-Planning/pddl/workflows/test/badge.svg\">\n </a>\n <a href=\"\">\n <img alt=\"lint\" src=\"https://github.com/AI-Planning/pddl/workflows/lint/badge.svg\">\n </a>\n <a href=\"\">\n <img alt=\"docs\" src=\"https://github.com/AI-Planning/pddl/workflows/docs/badge.svg\">\n </a>\n <a href=\"https://codecov.io/gh/AI-Planning/pddl\">\n <img alt=\"codecov\" src=\"https://codecov.io/gh/AI-Planning/pddl/branch/main/graph/badge.svg?token=FG3ATGP5P5\">\n </a>\n</p>\n<p align=\"center\">\n <a href=\"https://img.shields.io/badge/flake8-checked-blueviolet\">\n <img alt=\"\" src=\"https://img.shields.io/badge/flake8-checked-blueviolet\">\n </a>\n <a href=\"https://img.shields.io/badge/mypy-checked-blue\">\n <img alt=\"\" src=\"https://img.shields.io/badge/mypy-checked-blue\">\n </a>\n <a href=\"https://img.shields.io/badge/code%20style-black-black\">\n <img alt=\"black\" src=\"https://img.shields.io/badge/code%20style-black-black\" />\n </a>\n <a href=\"https://www.mkdocs.org/\">\n <img alt=\"\" src=\"https://img.shields.io/badge/docs-mkdocs-9cf\">\n </a>\n</p>\n\n`pddl` aims to be an unquestionable and complete parser for PDDL 3.1.\n\n## Install\n\n- from PyPI:\n```\npip install pddl\n```\n\n- from source (`main` branch):\n```\npip install git+https://github.com/AI-Planning/pddl.git\n```\n\n- or, clone the repository and install:\n```\ngit clone https://github.com/AI-Planning/pddl.git\ncd pddl\npip install .\n```\n## Quickstart\n\nYou can use the `pddl` package in two ways: as a library, and as a CLI tool.\n\n### As a library\n\nThis is an example of how you can build a PDDL domain or problem\nprogrammatically:\n\n```python\nfrom pddl.logic import Predicate, constants, variables\nfrom pddl.core import Domain, Problem\nfrom pddl.action import Action\nfrom pddl.formatter import domain_to_string, problem_to_string\nfrom pddl.requirements import Requirements\n\n# set up variables and constants\nx, y, z = variables(\"x y z\", types=[\"type_1\"])\na, b, c = constants(\"a b c\", type_=\"type_1\")\n\n# define predicates\np1 = Predicate(\"p1\", x, y, z)\np2 = Predicate(\"p2\", x, y)\n\n# define actions\na1 = Action(\n \"action-1\",\n parameters=[x, y, z],\n precondition=p1(x, y, z) & ~p2(y, z),\n effect=p2(y, z)\n)\n\n# define the domain object.\nrequirements = [Requirements.STRIPS, Requirements.TYPING]\ndomain = Domain(\"my_domain\",\n requirements=requirements,\n types={\"type_1\": None},\n constants=[a, b, c],\n predicates=[p1, p2],\n actions=[a1])\n\nprint(domain_to_string(domain))\n```\n\nthat gives:\n```output\n(define (domain my_domain)\n (:requirements :strips :typing)\n (:types type_1)\n (:constants a b c - type_1)\n (:predicates (p1 ?x - type_1 ?y - type_1 ?z - type_1) (p2 ?x - type_1 ?y - type_1))\n (:action action-1\n :parameters (?x - type_1 ?y - type_1 ?z - type_1)\n :precondition (and (p1 ?x ?y ?z) (not (p2 ?y ?z)))\n :effect (p2 ?y ?z)\n )\n)\n```\n\nAs well as a PDDL problem:\n```python\nproblem = Problem(\n \"problem-1\",\n domain=domain,\n requirements=requirements,\n objects=[a, b, c],\n init=[p1(a, b, c), ~p2(b, c)],\n goal=p2(b, c)\n)\nprint(problem_to_string(problem))\n```\n\nOutput:\n```output\n(define (problem problem-1)\n (:domain my_domain)\n (:requirements :strips :typing)\n (:objects a b c - type_1)\n (:init (not (p2 b c)) (p1 a b c))\n (:goal (p2 b c))\n)\n```\n\nExample parsing:\n```python\nfrom pddl import parse_domain, parse_problem\ndomain = parse_domain('d.pddl')\nproblem = parse_problem('p.pddl')\n```\n\n### As CLI tool\n\nThe package can also be used as a CLI tool.\nSupported commands are:\n- `pddl domain FILE`: validate a PDDL domain file, and print it formatted.\n- `pddl problem FILE`: validate a PDDL problem file, and print it formatted.\n\n## Features\n\nSupported [PDDL 3.1](https://helios.hud.ac.uk/scommv/IPC-14/repository/kovacs-pddl-3.1-2011.pdf)\nrequirements:\n\n- [x] `:strips`\n- [x] `:typing`\n- [x] `:negative-preconditions`\n- [x] `:disjunctive-preconditions`\n- [x] `:equality`\n- [x] `:existential-preconditions`\n- [x] `:universal-preconditions`\n- [x] `:quantified-preconditions`\n- [x] `:conditional-effects`\n- [ ] `:fluents`\n- [x] `:numeric-fluents`\n- [x] `:non-deterministic` (see [6th IPC: Uncertainty Part](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.163.7140&rep=rep1&type=pdf))\n- [x] `:adl`\n- [ ] `:durative-actions`\n- [ ] `:duration-inequalities`\n- [x] `:derived-predicates`\n- [ ] `:timed-initial-literals`\n- [ ] `:preferences`\n- [ ] `:constraints`\n- [x] `:action-costs`\n\n## Development\n\nIf you want to contribute, here's how to set up your development environment.\n\n- Install [Pipenv](https://pipenv-fork.readthedocs.io/en/latest/)\n- Clone the repository: `git clone https://github.com/AI-Planning/pddl.git && cd pddl`\n- Install development dependencies: `pipenv shell --python 3.8 && pipenv install --dev`\n\n## Tests\n\nTo run tests: `tox`\n\nTo run only the code tests: `tox -e py37`\n\nTo run only the code style checks: `tox -e flake8`\n\n## Docs\n\nTo build the docs: `mkdocs build`\n\nTo view documentation in a browser: `mkdocs serve`\nand then go to [http://localhost:8000](http://localhost:8000)\n\n## Authors\n\n- [Marco Favorito](https://marcofavorito.me)\n- [Francesco Fuggitti](https://francescofuggitti.github.io)\n- [Christian Muise](http://www.haz.ca/)\n\n## License\n\n`pddl` is released under the MIT License.\n\nCopyright (c) 2021-2022 WhiteMech\n\n## Acknowledgements\n\nThe `pddl` project is partially supported by the ERC Advanced Grant WhiteMech\n(No. 834228), the EU ICT-48 2020 project TAILOR (No. 952215),\nthe PRIN project RIPER (No. 20203FFYLK), and the JPMorgan AI Faculty\nResearch Award \"Resilience-based Generalized Planning and Strategic\nReasoning\".\n\n\n# Change Log\n\n## 0.1.0 (2021-06-21)\n\nThe first official release of pddl.\n\nMain features:\n* Specify PDDL domains and problems programmatically.\n* Parsing for PDDL domains and problems.\n\n## 0.0.1 (2020-07-30)\n\n* First commit on the package.\n\n",
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