macq


Namemacq JSON
Version 0.3.11 PyPI version JSON
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home_pageNone
SummaryAction model acquisition from state trace data.
upload_time2024-12-25 05:30:00
maintainerNone
docs_urlNone
authorEthan Callanan, Rebecca De Venezia, Victoria Armstrong, Alison Parades, Tathagata Chakraborti
requires_python>=3.8
licenseMIT License Copyright (c) 2020 Queen's Mu Lab Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords planning action model acquisition
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            # MAcq: The Model Acquisition Toolkit

[![CI](https://github.com/ai-planning/macq/actions/workflows/check.yml/badge.svg)](https://github.com/ai-planning/macq/actions)
[![Coverage](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/haz/03ac305b42d7c9ad4ef3213341bf3f2f/raw/macq__heads_main.json?cacheSeconds=3600)](https://github.com/ai-planning/macq/actions)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![License](https://img.shields.io/badge/license-MIT-purple)](https://github.com/ai-planning/macq/blob/main/LICENSE)

This library is a collection of tools for planning-like action model acquisition from state trace data. It contains a reimplementation from many existing works, and generalizes some of them to new settings.

## Example Usage

```python
from macq import generate, extract
from macq.observation import IdentityObservation, AtomicPartialObservation

# get a domain-specific generator: uses api.planning.domains problem_id/
# generate 100 traces of length 20 using vanilla sampling
traces = generate.pddl.VanillaSampling(problem_id = 123, plan_len = 20, num_traces = 100).traces

traces.generate_more(10)

action = traces[0][0].action
traces.get_usage(action)
# [0.05, 0.05, ..., 0.05]

trace = traces[0]
len(trace)
# 20

trace.fluents
trace.actions
trace.get_pre_states(action) # get the state before each occurance of action
trace.get_post_states(action) # state after each occurance of action
trace.get_total_cost()
```

## Survey

You can find the full scope of papers considered in the survey (implemented and otherwise) at http://macq.planning.domains . This repository of model acquisition techniques will be added to over time.

## Related Survey Papers

- [A Review of Machine Learning for Automated Planning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.231.4901&rep=rep1&type=pdf) (see Fig 2)
- [A Review of Learning Planning Action Models](https://hal.archives-ouvertes.fr/hal-02010536/document) (see Tbl 3)

## Citing this work

```latex
@inproceedings{macq-keps-2022,
  author    = {Ethan Callanan and Rebecca De Venezia and Victoria Armstrong and Alison Paredes and Tathagata Chakraborti and Christian Muise},
  title     = {MACQ: A Holistic View of Model Acquisition Techniques},
  booktitle = {The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)},
  year      = {2022}
}
```

            

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    "description": "# MAcq: The Model Acquisition Toolkit\n\n[![CI](https://github.com/ai-planning/macq/actions/workflows/check.yml/badge.svg)](https://github.com/ai-planning/macq/actions)\n[![Coverage](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/haz/03ac305b42d7c9ad4ef3213341bf3f2f/raw/macq__heads_main.json?cacheSeconds=3600)](https://github.com/ai-planning/macq/actions)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![License](https://img.shields.io/badge/license-MIT-purple)](https://github.com/ai-planning/macq/blob/main/LICENSE)\n\nThis library is a collection of tools for planning-like action model acquisition from state trace data. It contains a reimplementation from many existing works, and generalizes some of them to new settings.\n\n## Example Usage\n\n```python\nfrom macq import generate, extract\nfrom macq.observation import IdentityObservation, AtomicPartialObservation\n\n# get a domain-specific generator: uses api.planning.domains problem_id/\n# generate 100 traces of length 20 using vanilla sampling\ntraces = generate.pddl.VanillaSampling(problem_id = 123, plan_len = 20, num_traces = 100).traces\n\ntraces.generate_more(10)\n\naction = traces[0][0].action\ntraces.get_usage(action)\n# [0.05, 0.05, ..., 0.05]\n\ntrace = traces[0]\nlen(trace)\n# 20\n\ntrace.fluents\ntrace.actions\ntrace.get_pre_states(action) # get the state before each occurance of action\ntrace.get_post_states(action) # state after each occurance of action\ntrace.get_total_cost()\n```\n\n## Survey\n\nYou can find the full scope of papers considered in the survey (implemented and otherwise) at http://macq.planning.domains . This repository of model acquisition techniques will be added to over time.\n\n## Related Survey Papers\n\n- [A Review of Machine Learning for Automated Planning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.231.4901&rep=rep1&type=pdf) (see Fig 2)\n- [A Review of Learning Planning Action Models](https://hal.archives-ouvertes.fr/hal-02010536/document) (see Tbl 3)\n\n## Citing this work\n\n```latex\n@inproceedings{macq-keps-2022,\n  author    = {Ethan Callanan and Rebecca De Venezia and Victoria Armstrong and Alison Paredes and Tathagata Chakraborti and Christian Muise},\n  title     = {MACQ: A Holistic View of Model Acquisition Techniques},\n  booktitle = {The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)},\n  year      = {2022}\n}\n```\n",
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