# MAcq: The Model Acquisition Toolkit
[](https://github.com/ai-planning/macq/actions)
[](https://github.com/ai-planning/macq/actions)
[](https://github.com/psf/black)
[](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.
## 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.
## 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[](https://github.com/ai-planning/macq/actions)\n[](https://github.com/ai-planning/macq/actions)\n[](https://github.com/psf/black)\n[](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## 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)\n20\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## 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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