Skip to main content

Tarski is a framework for the specification, modeling and manipulation of AI planning problems.

Project description

Tarski - An AI Planning Modeling Framework

Build Status codecov PyPI - Python Version PyPI

What is Tarski

Tarski is a framework for the specification, modeling and manipulation of AI planning problems. Tarski is written in Python and includes parsers for major modeling languages (e.g., PDDL, FSTRIPS, RDDL), along with modules to perform common tasks such as reachability analysis and grounding of first-order representations.

Installation

You can install the latest Tarski release with

pip install tarski

If instead you want to use the latest code available on the Github repository, use

pip install -U git+https://github.com/aig-upf/tarski.git

Development

If developing Tarski, we recommend cloning from the Github repository and doing a dev installation (the-e flag for pip) on a virtual environment:

git clone https://github.com/aig-upf/tarski
cd tarski
pip install -e .

This will install the project in "editable mode", meaning that any modification to the files is immediately reflected in the installed library.

Testing

All of Tarski's tests live under the tests directory (shocking!). To run them, you just need to run pytest (pip install pytest) on the root directory. You can also run tox (pip install tox) to have some additional checks (e.g., style checks) run.

Software Requirements

Tarski requires Python >= 3.5. The above installation instructions will install transparently for you a number of additional dependencies, among which numpy, scipy and pyrddl.

Known Limitations

At the moment, Tarski is able to parse problems specified in PDDL, Functional STRIPS and RDDL, but (1) parsing of derived predicates is not supported yet, and (2) the PDDL either keyword for defining compound types is not supported, and it is unlikely it will ever be. Additionally, and for compatibility reasons with old standard benchmarks, the parser represents all predicate, function (including constants) and PDDL types (i.e. FOL sorts) in lowercase.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tarski-0.3.0.tar.gz (127.7 kB view details)

Uploaded Source

Built Distribution

tarski-0.3.0-py3-none-any.whl (160.8 kB view details)

Uploaded Python 3

File details

Details for the file tarski-0.3.0.tar.gz.

File metadata

  • Download URL: tarski-0.3.0.tar.gz
  • Upload date:
  • Size: 127.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.6.9

File hashes

Hashes for tarski-0.3.0.tar.gz
Algorithm Hash digest
SHA256 bf337f83073c812ec26ae789627c6d85325df5f39ee84d6c24a3440eab0da734
MD5 451e810561e730d6eb90484a520d31bd
BLAKE2b-256 47b1c2e8282488f9f992a4ec8900c781a2cb59ef5f720e45ae62f709e9496a0f

See more details on using hashes here.

File details

Details for the file tarski-0.3.0-py3-none-any.whl.

File metadata

  • Download URL: tarski-0.3.0-py3-none-any.whl
  • Upload date:
  • Size: 160.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.6.9

File hashes

Hashes for tarski-0.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 7fe1bc3eb090e3b7f1fa024fbc774c0b61e140dead2a26c14ececfdb1cd3752c
MD5 e7ce320c1e02378211e514b665807fa2
BLAKE2b-256 47bf852f68e0fd32e0d94f0c2b20e60dbc88ab6420d2f5b57367eb2636c69d1d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page