pddl 0.4.0

Description:

pddl 0.4.0

pddl

















































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:
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:
(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:
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:
(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:
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
requirements:

:strips
:typing
:negative-preconditions
:disjunctive-preconditions
:equality
:existential-preconditions
:universal-preconditions
:quantified-preconditions
:conditional-effects
:fluents
:numeric-fluents
:non-deterministic (see 6th IPC: Uncertainty Part)
:adl
:durative-actions
:duration-inequalities
:derived-predicates
:timed-initial-literals
:preferences
:constraints
:action-costs

Development
If you want to contribute, here's how to set up your development environment.

Install Pipenv
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
Authors

Marco Favorito
Francesco Fuggitti
Christian Muise

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.