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gnn-parity-games-solver

An incomplete parity game solver based on graph neural networks

https://github.com/dlr-sc/gnn-parity-games-solver

Science Score: 62.0%

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
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    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
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    Committers with academic emails
    2 of 2 committers (100.0%) from academic institutions
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    Institutional organization owner
    Organization dlr-sc has institutional domain (www.dlr.de)
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    Scientific vocabulary similarity
    Low similarity (7.7%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

An incomplete parity game solver based on graph neural networks

Basic Info
  • Host: GitHub
  • Owner: DLR-SC
  • Language: Python
  • Default Branch: main
  • Size: 12.5 MB
Statistics
  • Stars: 1
  • Watchers: 5
  • Forks: 1
  • Open Issues: 1
  • Releases: 1
Created over 3 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.md

License: MIT REUSE status Badge: Citation File Format Inside

gnnprsolver

Requirements

pip install --user torch torch_sparse torch-geometric torch-scatter

Train models

Use

./gnn-pg-solver.py train --network GAT --output GAT_weights.pth games/game_1.txt solutions/soluation_1.txt

or its equivalent using shorthand options

./gnn-pg-solver.py train -n GAT -o GAT_weights.pth games/game_1.txt solutions/soluation_1.txt

Predict winning regions

We assume that the directory games contains a set of plain-text files containing parity games. Use

./gnn-pg-solver.py predict --network GAT --weights GAT_weights.pth --output results.csv games/*

or its equivalent using shorthand options

./gnn-pg-solver.py predict -n GAT -w GAT_weights.pth -o results.csv games/*

Evaluate predictions

First, generate some predictions using the command in the previous section. We assume that for each game games/game_XXXX.txt there exists a solution solutions/solution_game_XXXX.txt.

The evaluate subcommand expects the input to be in the form game prediction reference, so we need to add the final column to the results before handing them to the evaluation.

sed 's:^games/game_\([0-9]*\).txt.*$:solutions/solution_game_\1.txt:' < results.csv > solutions.csv
paste -d' ' results.csv solutions.csv | ./gnn-pg-solver.py evaluate

Owner

  • Name: DLR Institute for Software Technology
  • Login: DLR-SC
  • Kind: organization
  • Email: opensource@dlr.de
  • Location: Cologne, Berlin, Braunschweig, Oberpfaffenhofen, Bremen

German Aerospace Center (DLR)

Citation (CITATION.cff) 

# SPDX-FileCopyrightText: 2022 German Aerospace Center (DLR)
# SPDX-FileContributor: Alexander Weinert <alexander.weinert@dlr.de>
#
# SPDX-License-Identifier: CC-BY-NC-ND-3.0

cff-version: 1.2.0
message: "If you use gnn_pr_solver in your research, please cite it using these metadata."
title: gnn_pr_solver
abstract: "gnn_pr_solver is a prototypical incomplete solver for parity games based that uses graph neural networks"
authors:
  - family-names: Hecking
    given-names: Tobias
    affiliation: "German Aerospace Center (DLR)"
    orcid: "https://orcid.org/0000-0003-0833-7989"
license: MIT
repository-code: "https://github.com/DLR-SC/gnn_pr_solver"

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: 10 months ago

All Time
  • Total Commits: 27
  • Total Committers: 2
  • Avg Commits per committer: 13.5
  • Development Distribution Score (DDS): 0.185
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Alexander Weinert a****t@d****e 22
Hecking, Tobias t****g@d****e 5
Committer Domains (Top 20 + Academic)
dlr.de: 2

Issues and Pull Requests

Last synced: 10 months ago

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  • Total issues: 0
  • Total pull requests: 1
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  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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  • SwathyMuthukrishnan (1)
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