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Computer Science > Programming Languages

arXiv:2308.15893 (cs)
[Submitted on 30 Aug 2023]

Title:The Janus System: Multi-paradigm Programming in Prolog and
Python

Authors:Theresa Swift (Johns Hopkins Applied Physics Lab), Carl
Andersen
Download a PDF of the paper titled The Janus System: Multi-paradigm
Programming in Prolog and Python, by Theresa Swift (Johns Hopkins
Applied Physics Lab) and 1 other authors
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    Abstract: Python and Prolog express different programming
    paradigms, with different strengths. Python is wildly popular
    because it is well-structured, easy to use, and mixes well with
    thousands of scientific and machine learning programs written in
    C. Prolog's logic-based approach provides powerful reasoning
    capabilities, especially when combined with constraint
    evaluation, probabilistic reasoning, well-founded negation, and
    other advances. Both languages have commonalities as well: both
    are usually written in C, both are dynamically typed, and both
    use data structures based on a small number of recursive types.
    This paper describes the design and implementation of Janus, a
    system that tightly combines Prolog and Python into a single
    process. Janus bi-translates data structures and offers
    performance of many hundreds of thousands of round-trip
    inter-language calls per second. Although Janus is still new, it
    has been used in commercial applications including natural
    language processing, visual query answering and robotic
    automation. Janus was developed for XSB, but porting Janus code
    to a second Prolog has been straightforward, indicating that
    Janus is a tool that other Prologs may easily adopt.

Comments:          In Proceedings ICLP 2023, arXiv:2308.14898
Subjects:          Programming Languages (cs.PL); Logic in Computer
                   Science (cs.LO)
Cite as:           arXiv:2308.15893 [cs.PL]
                   (or arXiv:2308.15893v1 [cs.PL] for this version)
                   https://doi.org/10.48550/arXiv.2308.15893
                   Focus to learn more
                   arXiv-issued DOI via DataCite
Journal reference: EPTCS 385, 2023, pp. 241-255
                   https://doi.org/10.4204/EPTCS.385.24
Related DOI:       Focus to learn more
                   DOI(s) linking to related resources

Submission history

From: EPTCS [view email] [via EPTCS proxy]
[v1] Wed, 30 Aug 2023 09:07:05 UTC (53 KB)
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