planning-machine 0.2

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Introduction

PlanningMachine is an automated planning library that parse YAML-based instructions into PDDL, solves them using a planner, and returns a structured plan with step effects. It can be run as a standalone service via Docker Compose or integrated directly into Python projects as a library. The goal of this project is to make the technology in automated planning more accessible to software engineers without exposing them to the internals of how they work.

Setup and Installation

The Python library can be installed either by building the wheel files from source or via pip. We recommend the latter.

from Source

1. In the root directory, execute python setup.py sdist bdist_wheel to create a .whl file in a newly created dist folder.
2. Execute pip install /dist/filename.whl, where filename.whl is a placeholder name for the created .whl file.

from PyPi

Execute pip install planning_machine.

Usage

PlanningMachine assumes Docker is installed (and running) on your computer.

Docker Compose

1. Create a directory named workdir in the root folder.
2. Place two files in workdir: domain.yaml and problem.yaml.
3. Execute docker compose up in the root folder. This parses the files in the workdir to PDDL, calls the planner to solve them, and extract the effects of each plan step. The solution is placed in workdir/plan.yaml.

Python

Import and use the PlanningMachine class as follows:

from planning_machine import PlanningMachine

pm = PlanningMachine()
solution = pm.solve(yaml_domain_path, yaml_problem_path)

Provide the paths to your YAML domain and problem files, and .solve() will return the solution to your planning problem.

A demo Jupyter notebook, demo.ipynb, is also included. It walks through a real-world logistics problem end-to-end, covering setup, solving, analytics, and visualization.

Contact

Please contact [e-mail] for questions, comments, or feedback about the PlanningMachine library.