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#logicprogramming — Public Fediverse posts

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  1. 🚀 Eyepl v0.1.14 is out!

    Facts + rules → answers + inspectable proofs.

    ✅ 1,020 tests passing
    ✅ 555 executable conformance cases
    ✅ 137 focused ISO Prolog cases
    ✅ RDF 1.2 support
    ✅ Tiny, auditable JavaScript core

    Try it: github.com/eyereasoner/eyepl

  2. 🚀 Eyepl v0.1.14 is out!

    Facts + rules → answers + inspectable proofs.

    ✅ 1,020 tests passing
    ✅ 555 executable conformance cases
    ✅ 137 focused ISO Prolog cases
    ✅ RDF 1.2 support
    ✅ Tiny, auditable JavaScript core

    Try it: github.com/eyereasoner/eyepl

    #Prolog #LogicProgramming #SemanticWeb #OpenSource

  3. eyelang: a small rule engine for Prolog-style Horn clauses over ordinary terms, lists, arithmetic, strings, and finite search. ~ Jos De Roo. eyereasoner.github.io/eyelang/ #LogicProgramming

  4. eyelang: a small rule engine for Prolog-style Horn clauses over ordinary terms, lists, arithmetic, strings, and finite search. ~ Jos De Roo. eyereasoner.github.io/eyelang/ #LogicProgramming

  5. Mercury logic programming: because who doesn't want to dive into a convoluted, antiquated language while pretending to care about "logic"? 🚀🔍 Just what the tech world needs; yet another esoteric codebase everyone will ignore! 🤦‍♂️
    github.com/Mercury-Language/me #MercuryProgramming #EsotericCode #LogicProgramming #TechHumor #ProgrammingLanguages #HackerNews #ngated

  6. Mercury logic programming: because who doesn't want to dive into a convoluted, antiquated language while pretending to care about "logic"? 🚀🔍 Just what the tech world needs; yet another esoteric codebase everyone will ignore! 🤦‍♂️
    github.com/Mercury-Language/me #MercuryProgramming #EsotericCode #LogicProgramming #TechHumor #ProgrammingLanguages #HackerNews #ngated

  7. @screwlisp @kentpitman regarding the discussion we had after the #LispyGopherClimate show ended, MiniKanren is logic programming language embedded in Scheme (sort-of like a Prolog implemented in Scheme and coded with S-expressions), and you can use machine leaning methods like neural networks to guide the search tree of the goal solver mechanism. This paper is an example of what I was talking about.

    Even before LLMs were invented, MiniKanren was able to do program synthesis using purely symbolic logic. They developed a prototype called Barliman where you would provide example input->output pairs as constraints, and using a constraint solver, could generalize those examples to a function that generates any output for any input. As a simple example, you could give it the following input-output pairs:

    1. () -> ()
    2. (a) () -> (a)
    3. () (a) -> (a)
    4. (a) (a) -> (a a)

    …and the constraint solver could determine that you are trying to implement the append function for lists and write the code automatically — without LLMs, using purely symbolic logic.

    As you might expect, the solver could be very slow, or even diverge (never returning an answer). The paper I mentioned above talks about using neural networks to try to guide the constraint solver to improve the performance and usefulness of the results returned by the solver.

    Now imagine applying this technique to other domains besides code generation or optimization, for example, auto-completion, or cache pre-fetching, and building it into a programmable computing environment like Emacs. You could have a tool like “Cursor,” but instead of using LLMs, it uses classical computing and constraint solvers, while taking a fraction of the amount of energy that LLMs use.

    #tech #software #AI #LLM #MachineLearning #NeuralNetwork #ConstraintLogic #ConstraintSolver #LogicProgramming #Prolog #MiniKanren #Emacs #Lisp #Scheme #SchemeLang #ProgramSynthesis

  8. @screwlisp @kentpitman regarding the discussion we had after the #LispyGopherClimate show ended, MiniKanren is logic programming language embedded in Scheme (sort-of like a Prolog implemented in Scheme and coded with S-expressions), and you can use machine leaning methods like neural networks to guide the search tree of the goal solver mechanism. This paper is an example of what I was talking about.

    Even before LLMs were invented, MiniKanren was able to do program synthesis using purely symbolic logic. They developed a prototype called Barliman where you would provide example input->output pairs as constraints, and using a constraint solver, could generalize those examples to a function that generates any output for any input. As a simple example, you could give it the following input-output pairs:

    1. () -> ()
    2. (a) () -> (a)
    3. () (a) -> (a)
    4. (a) (a) -> (a a)

    …and the constraint solver could determine that you are trying to implement the append function for lists and write the code automatically — without LLMs, using purely symbolic logic.

    As you might expect, the solver could be very slow, or even diverge (never returning an answer). The paper I mentioned above talks about using neural networks to try to guide the constraint solver to improve the performance and usefulness of the results returned by the solver.

    Now imagine applying this technique to other domains besides code generation or optimization, for example, auto-completion, or cache pre-fetching, and building it into a programmable computing environment like Emacs. You could have a tool like “Cursor,” but instead of using LLMs, it uses classical computing and constraint solvers, while taking a fraction of the amount of energy that LLMs use.

    #tech #software #AI #LLM #MachineLearning #NeuralNetwork #ConstraintLogic #ConstraintSolver #LogicProgramming #Prolog #MiniKanren #Emacs #Lisp #Scheme #SchemeLang #ProgramSynthesis

  9. Coding exercise ———————– Given this map (you choose an input representation), assign colours to regions A–F so that no adjacent regions have the same colour. Choose your favourite prog language. #logicprogramming #computerscience #gofai Based on Triska's lovely #Prolog video youtu.be/6XD7vBbywMc

  10. 🚀✨ Behold, Dyna: the brainchild of machine learning mavericks who decided #Prolog wasn't inscrutable enough. Now with weighted rules! Because who wants their logic programming to be intuitive or user-friendly? 😅🔍
    dyna.org/ #Dyna #MachineLearning #LogicProgramming #Innovation #HackerNews #ngated

  11. 🚀✨ Behold, Dyna: the brainchild of machine learning mavericks who decided #Prolog wasn't inscrutable enough. Now with weighted rules! Because who wants their logic programming to be intuitive or user-friendly? 😅🔍
    dyna.org/ #Dyna #MachineLearning #LogicProgramming #Innovation #HackerNews #ngated