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On CNF Encodings of Decision Diagrams

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Book cover Integration of AI and OR Techniques in Constraint Programming (CPAIOR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9676))

Abstract

Decisions diagrams such as Binary Decision Diagrams (BDDs), Multi-valued Decision Diagrams (MDDs) and Negation Normal Forms (NNFs) provide succinct ways of representing Boolean and other finite functions. Hence they provide a powerful tool for modelling complex constraints in discrete satisfaction and optimization problems. Generic propagators for these global constraints exist, but they are complex and hard to implement. An alternative approach to making use of them for solving is to encode them to CNF, using SAT style solving technology to implement them efficiently. This may also have advantages since it is naturally incremental and exposes intermediate literals which may well be useful as search decisions for solving the problem.

In this paper we explore different ways that we can map these constraints to CNF, and the different properties these mappings maintain. Surprisingly the most used encoding of BDDs does not maintain domain consistency in arbitrary BDDs. We also consider the strength of propagation with respect to the intermediate literals. We give experiments which compare the performance of the different encodings.

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Notes

  1. 1.

    A longer version of this paper including proofs of all Theorems can be found at http://people.eng.unimelb.edu.au/pstuckey/mddenc.pdf.

  2. 2.

    Notice, however, that every result in this paper holds for non-reduced MDDs without long edges, and with some modifications of the rules the results also extend to non-reduced MDDs with long edges.

  3. 3.

    Benchmarks are available from http://people.eng.unimelb.edu.au/pstuckey/mddenc.tar.gz.

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Acknowledgement

NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.

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Authors and Affiliations

  1. NICTA, West Melbourne, Australia

    Ignasi Abío, Valentin Mayer-Eichberger & Peter J. Stuckey

  2. University of New South Wales, Sydney, Australia

    Valentin Mayer-Eichberger

  3. University of Melbourne, Melbourne, Australia

    Graeme Gange & Peter J. Stuckey

Authors
  1. Ignasi Abío
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  2. Graeme Gange
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  3. Valentin Mayer-Eichberger
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  4. Peter J. Stuckey
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Corresponding author

Correspondence to Valentin Mayer-Eichberger .

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Editors and Affiliations

  1. Université Laval, Quebec, Québec, Canada

    Claude-Guy Quimper

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Abío, I., Gange, G., Mayer-Eichberger, V., Stuckey, P.J. (2016). On CNF Encodings of Decision Diagrams. In: Quimper, CG. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2016. Lecture Notes in Computer Science(), vol 9676. Springer, Cham. https://doi.org/10.1007/978-3-319-33954-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-33954-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33953-5

  • Online ISBN: 978-3-319-33954-2

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