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ParaGnosis: A Tool for Parallel Knowledge Compilation

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Model Checking Software (SPIN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13872))

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Abstract

ParaGnosis (https://doi.org/10.5281/zenodo.7312034, https://zenodo.org/badge/latestdoi/560170574, Alternative url: https://github.com/gisodal/paragnosis, Demo url: https://github.com/gisodal/paragnosis/blob/main/DEMO.md) is an open-source tool that supports inference queries on Bayesian networks through weighted model counting. In the knowledge compilation step, the input Bayesian network is encoded as propositional logic and then compiled into a knowledge base in decision diagram representation. The tool supports various diagram formats, including the Weighted-Positive Binary Decision Diagram (WPBDD) which can concisely represent discrete probability distributions.

Once compiled, the probabilistic knowledge base can be queried in the inference step. To efficiently implement both steps, ParaGnosis uses simulated annealing to split the knowledge base into a number of partitions. This further reduces the decision diagram size and crucially enables parallelism in both the compilation and the inference steps. Experiments demonstrate that this partitioned approach, in combination with the WPBDD representation, can outperform other approaches in the knowledge compilation step, at the cost of slightly more expensive inference queries. Additionally, the tool can attain 15-fold parallel speedups using 64 cores.

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Notes

  1. 1.

    Demo: https://github.com/gisodal/paragnosis/blob/main/DEMO.md.

  2. 2.

    CUDD is available at http://vlsi.colorado.edu/~fabio/.

  3. 3.

    The SDD compiler is available at http://reasoning.cs.ucla.edu/sdd/.

  4. 4.

    The WPBDD compiler is available at https://github.com/gisodal/paragnosis/.

  5. 5.

    Ace is available at http://reasoning.cs.ucla.edu/ace/.

  6. 6.

    Dlib is available at http://dlib.net/.

  7. 7.

    HUGIN is available at http://www.hugin.com/.

References

  1. Bahar, R.I., et al.: Algebraic decision diagrams and their applications. In: Proceedings of 1993 International Conference on Computer Aided Design, pp. 188–191 (1993)

    Google Scholar 

  2. Bartocci, E., Kovács, L., Stankovič, M.: Mora - automatic generation of moment-based invariants. In: TACAS 2020. LNCS, vol. 12078, pp. 492–498. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45190-5_28

    Chapter  Google Scholar 

  3. Bryant, R.E.: Graph-based algorithms for Boolean function manipulation. Trans. Comput. 100, 677–691 (1986)

    Google Scholar 

  4. Ceylan, I.I., Darwiche, A., Van den Broeck, G.: Open-world probabilistic databases: semantics, algorithms, complexity. Artif. Intell. 295, 103–123 (2021)

    Google Scholar 

  5. Chavira, M., Darwiche, A.: On probabilistic inference by weighted model counting. Artif. Intell. 172, 772–799 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen, Y., Darwiche, A.: On the definition and computation of causal treewidth. In: The 38th Conference on Uncertainty in Artificial Intelligence (2022)

    Google Scholar 

  7. Choi, Y.K., Santillana, C., Shen, Y., Darwiche, A., Cong, J.: FPGA acceleration of Probabilistic Sentential Decision Diagrams with high-level synthesis. ACM Trans. Reconfigurable Technol. Syst. 16(2), 1–22 (2022)

    Google Scholar 

  8. Clarke, E.M., Fujita, M., McGeer, P.C., McMillan, K., Yang, J.C.-Y., Zhao, X.: Multi-terminal binary decision diagrams: An efficient data structure for matrix representation (2001)

    Google Scholar 

  9. Dal, G.H., Kosters, W.A., Takes, F.W.: Fast diameter computation of large sparse graphs using GPUs. In: International Conference on Parallel, Distributed and Network-Based Processing, pp. 632–639 (2014)

    Google Scholar 

  10. Dal, G.H., Laarman, A.W., Hommersom, A., Lucas, P.J.: A compositional approach to probabilistic knowledge compilation. Int. J. Approximate Reason. 138, 38–66 (2021)

    Google Scholar 

  11. Dal, G.H., Laarman, A.W., Lucas, P.J.F.: Parallel probabilistic inference by weighted model counting. In: International Conference on Probabilistic Graphical Models, pp. 97–108 (2018)

    Google Scholar 

  12. Dal, G.H., Lucas, P.J.F.: Weighted positive binary decision diagrams for exact probabilistic inference. Int. J. Approx. Reason. 90, 411–432 (2017)

    Google Scholar 

  13. Dal, G.H., Michels, S., Lucas, P.J.F.: Reducing the cost of probabilistic knowledge compilation. Mach. Learn. Res. 73 141–152 (2017)

    Google Scholar 

  14. Darwiche, A.: SDD: A new canonical representation of propositional knowledge bases. In: International Joint Conference on Artificial Intelligence, vol. 22, pp. 819 (2011)

    Google Scholar 

  15. Darwiche, A., Hirth, A.: On the (complete) reasons behind decisions. J. Logic, Lang. Inform. Ages 18, 1–26 (2022)

    Google Scholar 

  16. Darwiche, A., Marquis, P.: A knowledge compilation map. J. Artif. Intell. Res. 17, 229–264 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  17. Darwiche, A., Marquis, P.: On quantifying literals in Boolean logic and its applications to explainable AI. J. Artif. Intell. Res. 72, 285–328 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  18. van Dijk, T., Laarman, A., van de Pol, J.: Multi-core BDD operations for symbolic reachability. Electron. Notes Theor. Comput. Sci. 296, 127–143 (2013)

    Article  Google Scholar 

  19. van Dijk, T., van de Pol, J.: Sylvan: multi-core framework for decision diagrams. Int. J. Softw. Tools Technol. Transfer 19, 675–696 (2017)

    Article  Google Scholar 

  20. Dudek, J., Phan, V., Vardi, M.: ADDMC: Weighted model counting with algebraic decision diagrams. In: International Conference on Artificial Intelligence, pp. 1468–1476 (2020)

    Google Scholar 

  21. Hillmich, S., Burgholzer, L., Stogmuller, F., Wille, R.: Reordering decision diagrams for quantum computing is harder than you might think. In: International Conference on Reversible Computation, pp. 93–107 (2022)

    Google Scholar 

  22. Hitzler, P., Sarker, M.K.: Tractable Boolean and arithmetic circuits. Neuro-Symbolic Artif. Intell.: State Art 342, 146 (2022)

    Google Scholar 

  23. Madsen, A.L., Lang, M., Kjaerulff, U.B., Jensen, F.: The Hugin tool for learning Bayesian networks. In: European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, pp. 594–605 (2003)

    Google Scholar 

  24. Miller, D.M., Thornton, M.A.: QMDD: A decision diagram structure for reversible and quantum circuits. In: 36th International Symposium on Multiple-Valued Logic, pp. 30–36 (2006)

    Google Scholar 

  25. Narayan, A., Jain, J., Fujita, M., Sangiovanni-Vincentelli, A.: Partitioned ROBDDs-a compact, canonical and efficiently manipulable representation for Boolean functions. In: Proceedings of International Conference on Computer Aided Design, pp. 547–554 (1996)

    Google Scholar 

  26. Judea Pearl. Bayesian networks (2011)

    Google Scholar 

  27. Peham, T., Burgholzer, L., Wille, R.: Equivalence checking paradigms in quantum circuit design: A case study. In: Proceedings of the 59th Design Automation Conference, pp. 517–522 (2022)

    Google Scholar 

  28. Sahoo, D.: A partitioning methodology for BDD-based verification. In: International Conference on Formal Methods in Computer-Aided Design, pp. 399–413 (2004)

    Google Scholar 

  29. Sanner, S., McAllester, D.: Affine algebraic decision diagrams (AADDs) and their application to structured probabilistic inference. In: International Joint Conference on Artificial Intelligence 2005, pp. 1384–1390 (2005)

    Google Scholar 

  30. Shih, A., Choi, A., Darwiche, A.: Compiling Bayesian network classifiers into decision graphs. In: International Conference on Artificial Intelligence, vol. 33, pp. 7966–7974 (2019)

    Google Scholar 

  31. Sokukcu, M., Sakar, C.: Risk analysis of collision accidents during underway sts berthing maneuver through integrating fault tree analysis (FTA) into Bayesian network. Appl. Ocean Res. 126, 103–124 (2022)

    Article  Google Scholar 

  32. Tafertshofer, P., Pedram, M.: Factored edge-valued binary decision diagrams. Formal Methods Syst. Design 10(2), 243–270 (1997)

    Article  Google Scholar 

  33. Viamontes, G.F., Markov, I.L., Hayes, J.P.: Improving gate-level simulation of quantum circuits. Quant. Inform. Process., 2, 347–380 (2003)

    Google Scholar 

  34. Wilson, N.: Decision diagrams for the computation of semiring valuations. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 331–336 (2005)

    Google Scholar 

  35. Zulehner, A., Hillmich, S., Wille, R.: How to efficiently handle complex values? Implementing decision diagrams for quantum computing. In: 2019 International Conference on Computer-Aided Design, pp. 1–7 (2019)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands

    Giso H. Dal & Peter J. F. Lucas

  2. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Alfons Laarman

Authors
  1. Giso H. Dal
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  2. Alfons Laarman
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  3. Peter J. F. Lucas
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Correspondence to Giso H. Dal .

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Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Georgiana Caltais

  2. Aalborg University, Aalborg, Denmark

    Christian Schilling

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Dal, G.H., Laarman, A., Lucas, P.J.F. (2023). ParaGnosis: A Tool for Parallel Knowledge Compilation. In: Caltais, G., Schilling, C. (eds) Model Checking Software. SPIN 2023. Lecture Notes in Computer Science, vol 13872. Springer, Cham. https://doi.org/10.1007/978-3-031-32157-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-32157-3_2

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  • Online ISBN: 978-3-031-32157-3

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