Summary

Perform weighted model counting in a lifted manner.

Usage

Information about the options and usage:

$ java -jar ./wfomc.jar -h

For example, to perform inference on the example model sickdeath.fg for the query death, you use the following command:

$ java -jar ./wfomc.jar -q "death" models/sickdeath.fg

MLNs can be queried as follows:

$ java -jar ./wfomc.jar -q "smokes(Guy)" models/friendsmoker.mln

Learning

To learn the weights of a theory, use the --wl flag and indicate the training database(s) with the -t flag.

$ java -jar ./wfomc.jar --mln-dist --wl 
    --train models/learning/smoking/smoking-train.db 
    models/learning/smoking/smoking.mln

External Dependencies

• For verifying the correctness of the results:
  • c2d compiler: Adnan Darwiche's c2d compiler.
    Used for propositional inference and verification. The binary is assumed to be installed as ./c2d_linux. This can be overridden with the environment variable C2DCMD.
• For visualizing the d-DNNFs:
  • pdflatex
    pdflatex is assumed to be in your path.
  • dot2tex
    dot2tex is assumed to be in your path.
  • dot2texi
    LaTeX package assumed to be installed.
  • Graphviz
    The dot2tex tool expects Graphviz dot to be available on your path.

Documentation

See doc/wfomc_manual.html for the documentation.

Publications

The algorithms implemented are explained in the following publications:

• W. Meert, G. Van den Broeck, and A. Darwiche. Lifted inference for probabilistic logic programs. In Proceedings of the 1st Workshop on Probabilsitic Logic Programming (PLP), 2014. (pdf)
• G. Van den Broeck, W. Meert, and A. Darwiche. Skolemization for Weighted First-Order Model Counting. In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014. (pdf)
• G. Van den Broeck, W. Meert and J. Davis. Lifted Generative Parameter Learning. In Proceedings of the 3rd International workshop on Statistical Relational AI (StarAI), held at the 27th AAAI Conference, 2013.
• G. Van den Broeck. Lifted Inference and Learning in Statistical Relational Models. PhD dissertation KU Leuven, 2013. (pdf)
• G. Van den Broeck and J. Davis. Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference. In Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI), 2012. (pdf)
• G. Van den Broeck, A. Choi, A. Darwiche. Lifted relax, compensate and then recover: From approximate to exact lifted probabilistic inference. In Proceedings of the conference on Uncertainty in Artificial Intelligence (UAI), 2012 (pdf)
• M. Jaeger, G. Van den Broeck. Liftability of probabilistic inference: Upper and lower bounds. In Proceedings of the 2nd International Workshop on Statistical Relational AI (StarAI), 2012. (pdf)
• W. Meert, G. Van den Broeck, N. Taghipour, D. Fierens, H. Blockeel, J. Davis, L. De Raedt. Lifted inference for probabilistic programming. In Proceedings of the NIPS Probabilistic Programming Workshop, 2012. (pdf)
• G. Van den Broeck. On the completeness of first-order knowledge compilation for lifted probabilistic inference. In Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS), 2011 (pdf)
• G. Van den Broeck, N. Taghipour, W. Meert, J. Davis, and L. De Raedt. Tutorial on Lifted Inference in Probabilistic Logical Models. On the 22th International Joint Conference on Artificial Intelligence (IJCAI), 2011. (pdf)
• G. Van den Broeck, N. Taghipour, W. Meert, J. Davis, and L. De Raedt. Lifted probabilistic inference by first-order knowledge compilation. In Proceedings of the 22th International Joint Conference on Artificial Intelligence (IJCAI), 2011. (pdf)

Contact

http://dtai.cs.kuleuven.be/wfomc/

Main contact:
Guy Van den Broek
Department of Computer Science
KU Leuven
Celestijnenlaan 200A
3001 Leuven
http://www.guyvdb.eu
guy.vandenbroeck@cs.kuleuven.be

Contributors

• Guy Van den Broeck
• Wannes Meert
• Jesse Davis
• Jan Van Haaren

Credits

• Argot:
  Used for command line parsing
  BSD License
• Breeze:
  Used for optimization and complex numbers
  Apache 2.0 License

License

Licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0

Copyright (c) 2011-2015, KU Leuven. All rights reserved.