Probabilistic Functional Logic Programming

Dylus, Sandra; Christiansen, Jan; Teegen, Finn

doi:10.1007/978-3-319-73305-0_1

Cited by 2 publications

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“…More precisely, it is important to enforce a certain degree of strictness in order to guarantee correct results. • In contrast to the conference version of the paper (Dylus et al 2018) we discuss the usage of partial functions in combination with library functions in more detail, reason about laws for two operations of the library, and present performance comparisons between our library, ProbLog and WebPPL. • Finally, this paper aims at fostering the exchange between the community of probabilistic programming and of functional logic programming.…”

Section: Introductionmentioning

confidence: 99%

Implementing a Library for Probabilistic Programming Using Non-strict Non-determinism

Dylus¹,

Christiansen²,

Teegen³

2019

Theory and Practice of Logic Programming

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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, language characteristics from one area exist in the other and vice versa. For example, the concepts of non-deterministic choice and call-time choice as known from functional logic programming are related to and coincide with stochastic memoization and probabilistic choice in probabilistic programming, respectively. We will further 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 and can even compete with full-blown probabilistic programming languages, which we illustrate by several benchmarks. Under consideration in Theory and Practice of Logic Programming (TPLP). KEYWORDS: probabilistic programming, functional logic programming, non-determinism, laziness, calltime choice * This is an extended version of a paper presented at the International Symposium on Practical Aspects of Declarative Languages (PADL 2018), invited as a rapid communication in TPLP. The authors acknowledge the assistance of the conference program chairs Nicola Leone and Kevin Hamlen. 14 We use version 2.7.10 of Python.

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Section: Introductionmentioning

confidence: 99%

Implementing a Library for Probabilistic Programming Using Non-strict Non-determinism

Dylus¹,

Christiansen²,

Teegen³

2019

Theory and Practice of Logic Programming

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“…This model uses a non-determinism monad instead of the partiality monad. Mona also planned to investigate modeling a probabilistic programming language as she read that functional logic and probabilistic programming are closely related in a manuscript by Dylus et al [12].…”

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One Monad to Prove Them All

Dylus¹,

Christiansen

Teegen

2019

Programming

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One Monad to Prove Them All is a modern fairy tale about curiosity and perseverance, two important properties of a successful PhD student. We follow the PhD student Mona on her adventure of proving properties about Haskell programs in the proof assistant Coq.On the one hand, as a PhD student in computer science Mona observes an increasing demand for correct software products. In particular, because of the large amount of existing software, verifying existing software products becomes more important. Verifying programs in the functional programming language Haskell is no exception. On the other hand, Mona is delighted to see that communities in the area of theorem proving are becoming popular. Thus, Mona sets out to learn more about the interactive theorem prover Coq and verifying Haskell programs in Coq.To prove properties about a Haskell function in Coq, Mona has to translate the function into Coq code. As Coq programs have to be total and Haskell programs are often not, Mona has to model partiality explicitly in Coq. In her quest for a solution Mona finds an ancient manuscript that explains how properties about Haskell functions can be proven in the proof assistant Agda by translating Haskell programs into monadic Agda programs. By instantiating the monadic program with a concrete monad instance the proof can be performed in either a total or a partial setting. Mona discovers that the proposed transformation does not work in Coq due to a restriction in the termination checker. In fact the transformation does not work in Agda anymore as well, as the termination checker in Agda has been improved.We follow Mona on an educational journey through the land of functional programming where she learns about concepts like free monads and containers as well as basics and restrictions of proof assistants like Coq. These concepts are well-known individually, but their interplay gives rise to a solution for Mona's problem based on the originally proposed monadic tranformation that has not been presented before. When Mona starts to test her approach by proving a statement about simple Haskell functions, she realizes that her approach has an additional advantage over the original idea in Agda. Mona's final solution not only works for a specific monad instance but even allows her to prove monad-generic properties. Instead of proving properties over and over again for specific monad instances she is able to prove properties that hold for all monads representable by a container-based instance of the free monad. In order to strengthen her confidence in the practicability of her approach, Mona evaluates her approach in a case study that compares two implementations for queues. In order to share the results with other functional programmers the fairy tale is available as a literate Coq file.If you are a citizen of the land of functional programming or are at least familiar with its customs, had a journey that involved reasoning about functional programs of your own, or are just a curious soul looking for the next story about monads ...

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Probabilistic Functional Logic Programming

Cited by 2 publications

References 14 publications

Implementing a Library for Probabilistic Programming Using Non-strict Non-determinism

Implementing a Library for Probabilistic Programming Using Non-strict Non-determinism

One Monad to Prove Them All

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