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63 lines
2 KiB
Markdown
63 lines
2 KiB
Markdown
satuio
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======
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Using SAT solvers to construct UIOs and ADSs for Mealy machines (aka
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finite state machines). Both problems are PSpace-complete, and so a
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SAT solver does not really make sense. However, with a given bound
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(encoded unary), the problems are in NP, and so can be encoded in SAT.
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There are some Mealy machines in `examples` directory. And even for the
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machine with roughly 500 states, the encodings are efficient, and
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sequences can be found within minutes.
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## Dependencies
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This project uses Python3. It uses the following packages which can be
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installed with `pip` as follows:
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```bash
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pip3 install python-sat tqdm rich
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```
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* [`pysat`](https://github.com/pysathq/pysat)
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* [`tqdm`](https://github.com/tqdm/tqdm)
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* [`rich`](https://github.com/Textualize/rich/)
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## Usage
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All scripts show their usage with the `--help` flag. Note that the
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flags and options are subject to change, since this is WIP. I
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recommend to read the source code of these scripts to see what is
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going on. (Or read the upcoming paper.)
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```bash
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# Finding UIO sequences of fixed length in a Mealy machine
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python3 satuio/uio.py --help
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# Finding UIO sequences while incrementing the length in a Mealy machine
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python3 satuio/uio-incr.py --help
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# Finding an ADS in a Mealy machine for a set of states
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python3 satuio/ads.py --help
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# Returning an unsat core in the case an ADS does not exist
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python3 satuio/ads-core.py --help
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```
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The solver can be specified (as long as pysat supports it). The default is
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[Glucose3](https://www.labri.fr/perso/lsimon/glucose/), as that worked
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well on the examples. After a bit more testing, these work well: `g3`, `mc`,
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`m22`. These are okay: `gc3`, `gc4`, `g4`, `mgh`. And these were slow for me:
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`cd`, `lgl`, `mcb`, `mcm`, `mpl`, `mg3`.
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The encoding currectly defaults to `seqcounter` for cardinality constraints.
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This seems to work just fine, and I am not even sure the encodings are really
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different for cardinality of `k=1`. It could be worth doing more testing to
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see which is the fastest.
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## Copyright
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© Joshua Moerman, Open Universiteit, 2022
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