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Probabilistic Functional Logic Programming

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Practical Aspects of Declarative Languages (PADL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10702))

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Abstract

This paper presents PFLP, a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming. In fact, the paradigms of functional logic and probabilistic programming are closely connected. That is, we can apply techniques from one area to the other and vice versa. We will see that an implementation based on the concepts of functional logic programming can have benefits with respect to performance compared to a standard list-based implementation.

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Notes

  1. 1.

    We provide the code for the library at https://github.com/finnteegen/pflp.

  2. 2.

    We use version 0.6.0 of KiCS2 and the source is found at https://www-ps.informatik.uni-kiel.de/kics2/.

  3. 3.

    Here and in the following we write probabilities as fractions for readability.

  4. 4.

    We visualize the interactions with the REPL using \(\lambda \mathbin {>}\) as prompt.

  5. 5.

    We shorten the implementation of \( enum \) for presentation purposes; actually, \( enum \) only allows valid distributions, e.g., that the given probabilities add up to \(\mathrm {1.0}\).

  6. 6.

    Due to the lack of overloading in Curry, operations on \( Float \) have a (floating) point suffix, e.g. \((\mathbin {*.})\), whereas operations on \( Int \) use the common operation names.

  7. 7.

    We use an abstract view of the result of an encapsulation to emphasize that the order of encapsulated results does not matter. In practice, we can, for example, use the function \( allValues \mathbin {{:}{:}} a \rightarrow [ a ]\) defined in the library \( Findall \).

  8. 8.

    All benchmarks were executed on a Linux machine with an Intel Core i7-6500U (2.50 GHz) and 8 GiB RAM running Fedora 25. We used the Glasgow Haskell Compiler (version 8.0.2, option -O2) and set the search strategy in KiCS2 to depth-first.

  9. 9.

    Non-determinism causes significant overhead for KiCS2, thus, “Curry ND” does not show linear development, but we measured a linear running time using PAKCS [14].

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Acknowledgements

We are thankful for fruitful discussions with Michael Hanus as well as suggestions of Jan Bracker and the anonymous reviewers to improve the readability of this paper.

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Authors and Affiliations

  1. University of Kiel, Kiel, Germany

    Sandra Dylus & Finn Teegen

  2. Flensburg University of Applied Sciences, Flensburg, Germany

    Jan Christiansen

Authors
  1. Sandra Dylus
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  2. Jan Christiansen
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  3. Finn Teegen
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Corresponding author

Correspondence to Sandra Dylus .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Calabria, Rende, Italy

    Francesco Calimeri

  2. Computer Science Department, University of Texas at Dallas, Richardson, Texas, USA

    Kevin Hamlen

  3. Department of Mathematics and Computer Science, University of Calabria, Rende, Italy

    Nicola Leone

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Dylus, S., Christiansen, J., Teegen, F. (2018). Probabilistic Functional Logic Programming. In: Calimeri, F., Hamlen, K., Leone, N. (eds) Practical Aspects of Declarative Languages. PADL 2018. Lecture Notes in Computer Science(), vol 10702. Springer, Cham. https://doi.org/10.1007/978-3-319-73305-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-73305-0_1

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  • Online ISBN: 978-3-319-73305-0

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