Implementing functional logic languages using multiple threads and stores

Tolmach, Andrew; Antoy, Sergio; Nita, Marius

doi:10.1145/1016848.1016865

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…Fischer et al [12] developed a custom monadic datatype for lazy nondeterministic programming. Their motivation was to nd a way combine three desirable features found in functional logic programming [13,18,32] and probabilistic programming [11,16] -lazyness, sharing (memoization), and nondeterminism, which are known to be tricky to combine in functional programming. Having two versions of transitions (Figures 4 and 5) in our implementation was to avoid an instance of undesirable side e ects from this trickinessnaive combination of laziness and nondeterminism causing needless traversals.…”

Section: Monadic Encodings Of Nondeterminismmentioning

confidence: 99%

Generating Witness of Non-Bisimilarity for the pi-Calculus

Ahn¹,

Horne²,

Tiu³

2017

Preprint

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In the logic programming paradigm, it is di cult to develop an elegant solution for generating distinguishing formulae that witness the failure of open-bisimilarity between two picalculus processes; this was unexpected because the semantics of the pi-calculus and open bisimulation have already been elegantly speci ed in higher-order logic programming systems. Our solution using Haskell de nes the formulae generation as a tree transformation from the forest of all nondeterministic bisimulation steps to a pair of distinguishing formulae. Thanks to laziness in Haskell, only the necessary paths demanded by the tree transformation function are generated. Our work demonstrates that Haskell and its libraries provide an attractive platform for symbolically analyzing equivalence properties of labeled transition systems in an environment sensitive setting. CCS Concepts•Theory of computation → Process calculi; Modal and temporal logics; Constraint and logic programming; Operational semantics; Program veri cation; •Software and its engineering → Functional languages; •Networks → Protocol testing and veri cation; Formal speci cations;

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Section: Monadic Encodings Of Nondeterminismmentioning

confidence: 99%

Generating Witness of Non-Bisimilarity for the pi-Calculus

Ahn¹,

Horne²,

Tiu³

2017

Preprint

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“…In typical interpreters, if and when the computation of either completes, the result is consumed, e.g., printed, and the user is given the option to either terminate the execution or continue with the computation of the other. Copying is simpler than backtracking and it is used in some experimental implementations of functional logic languages Tolmach et al 2004). The major objection to copying is the significant investment of time and memory made when a nondeterministic step is executed.…”

Section: Previous Approachesmentioning

confidence: 99%

On the correctness of pull-tabbing

Antoy¹

2011

Theory and Practice of Logic Programming

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Pull-tabbing is an evaluation approach for functional logic computations, based on a graph transformation recently proposed, which avoids making irrevocable non-deterministic choices that would jeopardize the completeness of computations. In contrast to other approaches with this property, it does not require an upfront cloning of a possibly large portion of the choice's context. We formally define the pull-tab transformation, characterize the class of programs for which the transformation is intended, extend the computations in these programs to include the transformation, and prove the correctness of the extended computations.

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“…There are different compilers for FLP languages that are partly based on the semantic frameworks discussed above. Moreover, the operational semantics by Albert et al (2005) has been implemented as Haskell interpreters by Tolmach & Antoy (2003) and Tolmach et al (2004). We do not define a compiler that translates an FLP language; nor do we define an interpreter in Haskell.…”

Section: Functional Logic Programmingmentioning

confidence: 99%

Purely functional lazy nondeterministic programming

Fischer¹,

Kiselyov²,

Shan³

2011

J. Funct. Prog.

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Functional logic programming and probabilistic programming have demonstrated the broad benefits of combining laziness (nonstrict evaluation with sharing of the results) with nondeterminism. Yet these benefits are seldom enjoyed in functional programming because the existing features for nonstrictness, sharing, and nondeterminism in functional languages are tricky to combine. We present a practical way to write purely functional lazy nondeterministic programs that are efficient and perspicuous. We achieve this goal by embedding the programs into existing languages (such as Haskell, SML, and OCaml) with high-quality implementations, by making choices lazily and representing data with nondeterministic components, by working with custom monadic data types and search strategies, and by providing equational laws for the programmer to reason about their code.

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Implementing functional logic languages using multiple threads and stores

Cited by 6 publications

References 16 publications

Generating Witness of Non-Bisimilarity for the pi-Calculus

Generating Witness of Non-Bisimilarity for the pi-Calculus

On the correctness of pull-tabbing

Purely functional lazy nondeterministic programming

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