> "4. Prolog is good at solving reasoning problems."
Plain Prolog's way of solving reasoning problems is effectively:
for person in [martha, brian, sarah, tyrone]:
if timmy.parent == person:
print "solved!"
You hard code some options, write a logical condition with placeholders, and Prolog brute-forces every option in every placeholder. It doesn't do reasoning.
Arguably it lets a human express reasoning problems better than other languages by letting you write high level code in a declarative way, instead of allocating memory and choosing data types and initializing linked lists and so on, so you can focus on the reasoning, but that is no benefit to an LLM which can output any language as easily as any other. And that might have been nice compared to Pascal in 1975, it's not so different to modern garbage collected high level scripting languages. Arguably Python or JavaScript will benefit an LLM most because there are so many training examples inside it, compared to almost any other langauge.
>> You hard code some options, write a logical condition with placeholders, and Prolog brute-forces every option in every placeholder. It doesn't do reasoning.
SLD-Resolution with unification (Prolog's automated theorem proving algorithm) is the polar opposite of brute force: as the proof proceeds, the cardinality of the set of possible answers [1] decreases monotonically. Unification itself is nothing but a dirty hack to avoid having to ground the Herbrand base of a predicate before completing a proof; which is basically going from an NP-complete problem to a linear-time one (on average).
Besides which I find it very difficult to see how a language with an automated theorem prover for an interpreter "doesn't do reasoning". If automated theorem proving is not reasoning, what is?
Prolog was introduced to capture natural language - in a logic/symbolic way that didn't prove as powerful as today's LLM for sure, but this still means there is a large corpus of direct English to Prolog mappings available for training, and also the mapping rules are much more straightforward by design. You can pretty much translate simple sentences 1:1 into Prolog clauses as in the classic boring example
% "the boy eats the apple"
eats(boy, apple).
This is being taken advantage of in Prolog code generation using LLMs. In the Quantum Prolog example, the LLM is also instructed not to generate search strategies/algorithms but just planning domain representation and action clauses for changing those domain state clauses which is natural enough in vanilla Prolog.
The results are quite a bit more powerful, close to end user problems, and upward in the food chain compared to the usual LLM coding tasks for Python and JavaScript such as boilerplate code generation and similarly idiosyncratic problems.
Its a Horn clause resolver...that's exactly the kind of reasoning that LLMs are bad at. I have no idea how to graft Prolog to an LLM but if you can graft any programming language to it, you can graft Prolog more easily.
Also, that you push Python and JavaScript makes me think you don't know many languages. Those are terrible languages to try to graft to anything. Just because you only know those 2 languages doesn't make them good choices for something like this. Learn a real language Physicist.
This sparked a really fascinating discussion, I don't know if anyone will see this but thanks everyone for sharing your thoughts :)
I understand your point - to an LLM there's no meaningful difference between once turing complete language and another. I'll concede that I don't have a counter argument, and perhaps it doesn't need to be prolog - though my hunch is that LLM's tend to give better results when using purpose built tools for a given type of problem.
The only loose end I want to address is the idea of "doing reasoning."
This isn't an AGI proposal (I was careful to say "good at writing prolog") just an augmentation that (as a user) I haven't yet seen applied in practice. But neither have I seen it convincingly dismissed.
The idea is the LLM would act like an NLP parser that gradually populates a prolog ontology, like building a logic jail one brick at a time.
The result would be a living breathing knowledge base which constrains and informs the LLM's outputs.
The punchline is that I don't even know any prolog myself, I just think it's a neat idea.
Of course it does "reasoning", what do you think reasoning is? From a quick google: "the action of thinking about something in a logical, sensible way". Prolog searches through a space of logical proposition (constraints) and finds conditions that lead to solutions (if one exists).
(a) Trying adding another 100 or 1000 interlocking proposition to your problem. It will find solutions or tell you one doesn't exist.
(b) You can verify the solutions yourself. You don't get that with imperative descriptions of problems.
(b) Good luck sandboxing Python or JavaScript with the treat of prompt injection still unsolved.
Contrary to what everyone else is saying, I think you're completely correct. Using it for AI or "reasoning" is a hopeless dead end, even if people wish otherwise. However I've found that Prolog is an excellent language for expressing certain types of problems in a very concise way, like parsers, compilers, and assemblers (and many more). The whole concept of using a predicate in different modes is actually very useful in a pragmatic way for a lot of problems.
When you add in the constraint solving extensions (CLP(Z) and CLP(B) and so on) it becomes even more powerful, since you can essentially mix vanilla Prolog code with solver tools.
Even in your example (which is obviously not correct representation of prolog), that code will work X orders magnitude faster and with 100% reliability compared to much more inferior LLM reasoning capabilities.
Everything you've written here is an invalid over-reduction, I presume because you aren't terribly well versed with Prolog. Your simplification is not only outright erroneous in a few places, but essentially excludes every single facet of Prolog that makes it a turing complete logic language. What you are essentially presenting Prolog as would be like presenting C as a language where all you can do is perform operations on constants, not even being able to define functions or preprocessor macros. To assert that's what C is would be completely and obviously ludicrous, but not so many people are familiar enough with Prolog or its underlying formalisms to call you out on this.
Firstly, we must set one thing straight: Prolog definitionally does reasoning. Formal reasoning. This isn't debatable, it's a simple fact. It implements resolution (a computationally friendly inference rule over computationally-friendly logical clauses) that's sound and refutation complete, and made practical through unification. Your example is not even remotely close to how Prolog actually works, and excludes much of the extra-logical aspects that Prolog implements. Stripping it of any of this effectively changes the language beyond recognition.
> Plain Prolog's way of solving reasoning problems is effectively:
No. There is no cognate to what you wrote anywhere in how Prolog works. What you have here doesn't even qualify as a forward chaining system, though that's what it's closest to given it's somewhat how top-down systems work with their ruleset. For it to even approach a weaker forward chaining system like CLIPS, that would have to be a list of rules which require arbitrary computation and may mutate the list of rules it's operating on. A simple iteration over a list testing for conditions doesn't even remotely cut it, and again that's still not Prolog even if we switch to a top-down approach by enabling tabling.
> You hard code some options
A Prolog knowledgebase is not hardcoded.
> write a logical condition with placeholders
A horn clause is not a "logical condition", and those "placeholders" are just normal variables.
> and Prolog brute-forces every option in every placeholder.
Absolutely not. It traverses a graph proving things, and when it cannot prove something it backtracks and tries a different route, or otherwise fails. This is of course without getting into impure Prolog, or the extra-logical aspects it implements. It's a fundamentally different foundation of computation which is entirely geared towards formal reasoning.
> And that might have been nice compared to Pascal in 1975, it's not so different to modern garbage collected high level scripting languages.
It is extremely different, and the only reason you believe this is because you don't understand Prolog in the slightest, as indicated by the unsoundness of essentially everything you wrote. Prolog is as different from something like Javascript as a neural network with memory is.
IIRC IBM’s Watson (the one that played Jeopardy) used primitive NLP (imagine!) to form a tree of factual relations and then passed this tree to construct Prolog queries that would produce an answer to a question. One could imagine that by swapping out the NLP part with an LLM, the model would have 1. a more thorough factual basis against which to write Prolog queries and 2. a better understanding of the queries it should write to get at answers (for instance, it may exploit more tenuous relations between facts than primitive NLP).
Not so "primitive" NLP. Watson started with what its team called a "shallow parse" of a sentence using a dependency grammar and then matched the parse to an ontology consisting of good, old fashioned frames [1]. That's not as "advanced" as an LLM but far more reliable.
I believe the ontology was indeed implemented in Prolog but I forget the architecture details.
We've done this, and it works. Our setup is to have some agents that synthesize Prolog and other types of symbolic and/or probabilistic models. We then use these models to increase our confidence in LLM reasoning and iterate if there is some mismatch. Making synthesis work reliably on a massive set of queries is tricky, though.
Imagine a medical doctor or a lawyer. At the end of the day, their entire reasoning process can be abstracted into some probabilistic logic program which they synthesize on-the-fly using prior knowledge, access to their domain-specific literature, and observed case evidence.
There is a growing body of publications exploring various aspects of synthesis, e.g. references included in [1] are a good starting point.
I am once again shilling the idea that someone should find a way to glue Prolog and LLMs together for better reasoning agents.
There are definitely people researching ideas here. For my own part, I've been doing a lot of work with Jason[1], a very Prolog like logic language / agent environment with an eye towards how to integrate that with LLMs (and "other").
Nothing specific / exciting to share yet, but just thought I'd point out that there are people out there who see potential value in this sort of thing and are investigating it.
>>Prolog doesn't look like javascript or python so:
Think of this way. In Python and Javascript you write code, and to test if its correct you write unit test cases.
A prolog program is basically a bunch of test cases/unit test cases, you write it, and then tell the Prolog compiler, 'write code, that passes these test cases'.
That is, you are writing the program specification, or tests that if pass would represent solution to the problem. The job of the compiler to write the code that passes these test cases.
It's been a while since I have done web dev, but web devs back then were certainly not scared of any language. Web devs are like the ultimate polyglots. Or at least they were. I was regularly bouncing around between a half dozen languages when I was doing pro web dev. It was web devs who popularized numerous different languages to begin with simply because delivering apps through a browser allowed us a wide variety of options.
The core idea of DeepClause is to use a custom Prolog-based DSL together with a metainterpreter implemented in Prolog that can keep track of execution state and implicitly manage conversational memory for an LLM. The DSL itself comes with special predicates that are interpreted by an LLM. "Vague" parts of the reasoning chain can thus be handed off to a (reasonably) advanced LLM.
Would love to collect some feedback and interesting ideas for possible applications.
As someone who did deep learning research 2017-2023, I agree. "Neurosymbolic AI" seems very obvious, but funding has just been getting tighter and more restrictive towards the direction of figuring out things that can be done with LLMs. It's like we collectively forgot that there's more than just txt2txt in the world.
YES! I've run a few experiments on classical logic problems and an LLM can spit out Prolog programs to solve the puzzel. Try it yourself, ask an LLM to write some prolog to solve some problem and then copy paste it to https://swish.swi-prolog.org/ and see if it runs.
Can't find the links right now, but there were some papers on llm generating prolog facts and queries to ground the reasoning part. Somebody else might have them around.
There's a lot of work in this area. See e.g., the LoRP paper by Di et al. There's also a decent amount of work on the other side too, i.e., using LLMs to convert Prolog reasoning chains back into natural language.
I've been thinking a lot about this, and I want to build the following experiment, in case anyone is interested:
The experiment is about putting an LLM to play plman[0] with and without prolog help.
plman is a pacman like game for learning prolog, it was written by profesor Francisco J. Gallego from Alicante University to teach logic subject in computer science.
Basically you write solution in prolog for a map, and plman executes it step by step so you can see visually the pacman (plman) moving around the maze eating and avoiding ghost and other traps.
There is an interesting dynamic about finding keys for doors and timing based traps.
There are different levels of complexity, and you can also write easily your maps, since they are just ascii characters in a text file.
I though this was the perfect project to visually explain my coworkers the limit of LLM "reasoning" and what is symbolic reasoning.
So far I hooked ChatGPT API to try to solve scenarios, and it fails even with substancial amount of retries. That's what I was expecting.
The next thing would be to write a mcp tool so that the LLM can navigate the problem by using the tool, but here is where I need guidance.
I'm not sure about the best dynamic to prove the usefulness of prolog in a way that goes beyond what context retrieval or db query could do.
I'm not sure if the LLM should write the prolog solution. I want to avoid to build something trivial like the LLM asking for the steps, already solved, so my intuition is telling me that I need some sort of virtual joystick mcp to hide prolog from the LLM, so the LLM could have access to the current state of the screen, and questions like what would be my position if I move up ?
What's the position of the ghost in next move ? where is the door relative to my current position ?
I don't have academic background to design this experiment properly. Would be great if anyone is interested to work together on this, or give me some advice.
Prior work pending on my reading list:
- LoRP: LLM-based Logical Reasoning via Prolog [1]
- A Pipeline of Neural-Symbolic Integration to Enhance Spatial Reasoning in Large Language Models [2]
Prolog really is such a fantastic system, if I can justify its usage then I won't hesitate to do so. Most of the time I'll call a language that I find to be powerful a "power tool", but that doesn't apply here. Prolog is beyond a power tool. A one-off bit of experimental tech built by the greatest minds of a forgotten generation. You'd it find deep in irradiated ruins of a dead city, buried far underground in a bunker easily missed. A supercomputer with the REPL's cursor flickering away in monochrome phosphor. It's sitting there, forgotten. Dutifully waiting for you to jack in.
When I entered university for my Bachelors, I was 28 years old and already worked for 5 or 6 years as a self-taught programmer in the industry. In the first semester, we had a Logic Programming class and it was solely taught in Prolog.
At first, I was mega overwhelmed. It was so different than anything I did before and I had to unlearn a lot of things that I was used to in "regular" programming. At the end of the class, I was a convert! It also opened up my mind to functional programming and mathematical/logical thinking in general.
I still think that Prolog should be mandatory for every programmer. It opens up the mind in such a logical way... Love it.
Unfortunately, I never found an opportunity in my 11 years since then to use it in my professional practice. Or maybe I just missed the opportunities?????
Did they teach you how to use DCGs? A few months ago I used EDCGs as part of a de-spaghettification and bug fixing effort to trawl a really nasty 10k loc sepples compilation unit and generate tags for different parts of it. Think ending up with a couple thousand ground terms like:
tag(TypeOfTag, ParentFunction, Line).
Type of tag indicating things like an unnecessary function call, unidiomatic conditional, etc.
I then used the REPL to pull things apart, wrote some manual notes, and then consulted my complete knowledgebase to create an action plan. Pretty classical expert system stuff. Originally I was expecting the bug fixing effort to take a couple of months. 10 days of Prolog code + 2 days of Prolog interaction + 3 days of sepples weedwacking and adjusting the what remained in the plugboard.
Prolog is a great language to learn. But I wouldn't want to use it for anything more than what its directly good at. Especially the cut operator, that's pretty mind bending. But once you get good at it, it all just flows. But I doubt more than 1% of devs could ever master it, even on an unlimited timeline. Its just much harder than any other type of non-research dev work.
But some parts, like e.g. the cut operator is something I've copied several times over for various things. A couple of prototype parser generators for example - allowing backtracking, but using a cut to indicate when backtracking is an error can be quite helpful.
There was a time when the thinking was you can load all the facts into a prolog engine and it would replace experts like doctors and engineers - expert systems, it didn't work. Now its a curiosity
My prolog anecdote: ~2001 my brother and I writing an A* pathfinder in prolog to navigate a bot around the world of Asheron's Call (still the greatest MMORPG of all time!). A formative experience in what can be done with code. Others had written a plugin system (called Decal) in C for the game and a parser library for the game's terrain file format. We took that data and used prolog to write an A* pathfinder that could navigate the world, avoiding un-walkable terrain and even using the portals to shortcut between locations. Good times.
There seems to an interesting difference between Prolog and conventional (predicate) logic.
In Prolog, anything that can't be inferred from the knowledge base is false. If nothing about "playsAirGuitar(mia)" is implied by the knowledge base, it's false. All the facts are assumed to be given; therefore, if something isn't given, it must be false.
Predicate logic is the opposite: If I can't infer anything about "playsAirGuitar(mia)" from my axioms, it might be true or false. It's truth value is unknown. It's true in some model of the axioms, and false in others. The statement is independent of the axioms.
Deductive logic assumes an open universe, Prolog a closed universe.
It's not really false I think. It's 'no', which is an answer to a question "Do I know this to be true?"
I think there should be a room for three values there: true, unprovable, false. Where false things are also unprovable. I wonder if Prolog has false, defined as "yes" of the opposite.
> It's not really false I think. It's 'no', which is an answer to a question "Do I know this to be true?"
I don't think so, because in this case both x and not-x could be "no", but I think in Prolog, if x is "no", not-x is "yes", even if neither is known to be true. It's not a three-valued logic that doesn't adhere to the law of the excluded middle.
I recently implemented an eagerly evaluated embedded Prolog dialect in Dart for my game applications. I used SWI documentation extensively to figure out what to implement.
But I think I had the most difficulty designing the interface between the logic code and Dart. I ended up with a way to add "Dart-defined relations", where you provide relations backed dynamically by your ECS or database. State stays in imperative land, rules stay in logic land.
Testing on Queens8, SWI is about 10,000 times faster than my implementation. It's a work of art! But it doesn't have the ease of use in my game dev context as a simple Dart library does.
I've recently started modeling some of my domains/potential code designs in Prolog. I'm not that advanced. I don't really know Prolog that well. But even just using a couple basic prolog patterns to implement a working spec in the 'prolog way' is *unbelievably* useful for shipping really clean code designs to replace hoary old chestnut code. (prolog -> ruby)
I keep wishing for "regex for prolog", ie: being able to (in an arbitrary language) express some functional bits in "prolog-ish", and then be able to ask/query against it.
let prologBlob = new ProLog()
prologBlob.add( "a => b" ).add( "b => c" )
prologBlob.query( "a == c?" ) == True
(not exactly that, but hopefully you get the gist)
There's so much stuff regarding constraints, access control, relationship queries that could be expressed "simply" in prolog and being able to extract out those interior buts for further use in your more traditional programming language would be really helpful! (...at least in my imagination ;-)
While usually using native syntax rather than strings, somethign like that exists for most languages of any popularity (and many obscure ones), in the form of miniKanren implementations.
I've wished for the same kind of 'embed prolog in my ruby' for enumerating all possible cases, all invalid cases, etc in test suites. Interesting to know it's not just me!
I wonder if there's examples of whole product architectures done in Prolog, seems like an elegant solution if done right. I've been looking for a concise way to model full architectures of my various projects, without relying on having a typical markdown file.
Which is separate from the actual types in the code.
Which is separate from the deployment section of the docs.
I studied prolog back in 2014. It was used in AI course. I found it very confusing: trying to code A*, N-Queens, or anything in it was just too much.
Python, in contrast, was a god-send.
I failed the subject twice in my MSc (luckily passing the MSc was based on the total average), but did a similar course in UC Berkeley, with python: aced it, loved it, and learned a lot.
A similar thing happened at my university in an Advanced Algorithms course. Students failed it so much, the university was forced to make the course easier to pass, by removing the minimum grade to pass.
I believe your case (and many other students) is that you couldn't abstract yourself from imperative programming (python) into logic programming (prolog).
Performance, far better performance. Same reason you ever use SQL. Prolog can do the same thing for very specific problems.
PS Prolog is a Horn clause solver. You characterizing it as a query language for a graph database, well it doesn't put you in the best light. It makes it seem like you don't understand important foundational CS math concepts.
I remember writing a Prolog(ish) interpreter in Common Lisp in an 90's AI course in grad school for Theorem proving (which is essentially what Prolog is doing under the hood). Really foundational to my understanding of how declarative programming works. In an ideal world I would still be programming in Lisp and using Prolog tools.
Speaking as someone who just started exploring Prolog and lisp, and ended up in the frozen north isolated from internet - access. The tools were initially locked/commercial only during a critical period, and then everyone was oriented around GUIs - and GUI environments were very hostile to the historical tools, and thus provided a different kind of access barrier.
A side one is that the LISP ecology in the 80s was hostile to "working well with others" and wanted to have their entire ecosystem in their own image files. (which, btw, is one of the same reasons I'm wary of Rust cough)
Really, it's only become open once more with the rise of WASM, systemic efficiency of computers, and open source tools finally being pretty solid.
I can tell you, from the year 2045, that running the worlds global economy on Javascript was the direct link to the annihilation of most of our freedom and existence. Hope this helps.
It is not nostalgia. It is mathematical thought. It is more akin to to an equation and more provably correct. Closer to fundamental truth -- like touching fundamental reality.
I only read the first 88 pages of Prolog Programming in Depth but I found it to be the best introductory book for programming in Prolog because it presents down to earth examples of coding like e.g. reading a file, storing data. Most other books are mainly or only focused on the pure logic stuff of Prolog but when you program you need more.
Another way of getting stuff done would be to use another programming language with its standard library (with regex, networking, json, ...) and embed or call Prolog code for the pure logic stuff.
I remember a project I did in undergrad with Prolog that would fit connecting parts of theoretical widgets together based on constraints about how different pieces could connect and it just worked instantly and it felt like magic because I had absolutely no clue how I would have coded that in Pascal or COBOL at that time. It blew my mind because the program was so simple.
Prolog is easily one of my favorite languages, and as many others in this thread, I first encountered it during university. I ended up teaching it for a couple of years (along with Haskell) and ever since, I've gone on an involuntary prolog bender of sorts once or twice a year. I almost always use it for Advent of code as well.
Declarative languages are fantastic to reason about code.
But the true power is unlocked once the underlying libraries are implemented in a way that surpassesthe performance that a human can achieve.
Since implementation details are hidden, caches and parallelism can be added without the programmer noticing anything else than a performance increase.
This is why SQL has received a boost the last decade with massively parallel implementations such as BigQuery, Trino and to some extent DuckDB. And what about adding a CUDA backend?
But all this comes at a cost and needs to be planned so it is only used when needed.
Because its more powerful than MongoDb or Fortran. The cut operator for instance gives it the ability to express things you just can't do in those other systems. The trade-off is that mastering the cut operator is a rare skill and only that one person who can do it can maintain the Prolog code. Compare that with MongoDb where even the village idiot can use it but with a huge performance cost.
I don't know about MongoDB and its query language, but wrt Fortran, it's unreasonable to say that Prolog is more powerful than Fortran (or vice versa). A more reasonable statement is that Prolog is more expressive than Fortran (though this gets fuzzy, we have to define expressiveness in a way that lets us rank languages). But the power of a language normally means what we can compute using that language. Prolog and Fortran both have the same level of "power", but it's certainly fair to say that expressing many programs is easier in Prolog than Fortran, and there are some (thinking back to my scientific computing days) that are easier to express in Fortran than Prolog.
“A touch! A distinct touch!” cried Holmes. "You are developing a certain unexpected vein of pawky humour, Watson, against which I must learn to guard myself".
-- from "The Valley of Fear" by Arthur Conan Doyle.
I recently asked @grok about Prolog being useless incomprehensible shit for anything bigger than one page:
Professionals write Prolog by focusing on the predicates and relations and leaving the execution flow to the interpreter. They also use the Constraint Logic Programming extensions (like clpfd) which use smart, external algorithms to solve problems instead of relying on Prolog's relatively "dumb" brute-force search, which is what typically leads to the "exploding brain" effect in complex code.
--- Worth mentioning here is that I wrote Prolog all on my own in 1979. On top of Nokolisp of course. There was no other functioning Prolog at that time I knew about.
Thereafter I have often planned "Infinity-Prolog" which can solve impossible problem with lazy evaluation.
I just learned from @grok that this Constraint Logic is basically what was aiming at.
I really enjoyed learning Prolog in university, but it is a weird language. I think that 98% of tasks I would not want to use Prolog for, but for that remaining 2% of tasks it's extremely well suited for. I have always wished that I could easily call Prolog easily from other languages when it suited the use case, however good luck getting most companies to allow writing some code in Prolog.
That is where Lisp or Scheme weirdly shines. It is incredibly easy to add prolog to a Lisp or a Scheme. It’s almost as if it comes out naturally if you just go down the rabbit hole.
“The little prover” is a fantastic book for that. The whole series is.
Racket really shines in this regard: Racket makes it easy to build little DSLs, but they all play perfectly together because the underlying data model is the same. Example from the Racket home page: https://racket-lang.org/#any-syntax
You can have a module written in the `#racket` language (i.e., regular Racket) and then a separate module written in `#datalog` and the two can talk to each other!
I love Prolog, and have seen so many interesting use cases for it.
In the end though, it mostly just feels enough of a separate universe to any other language or ecosystem I'm using for projects that there's a clear threshold for bringing it in.
If there was a really strong prolog implementation with a great community and ecosystem around, in say Python or Go, that would be killer. I know there are some implementations, but the ones I've looked into seem to be either not very full-blown in their Prolog support, or have close to non-existent usage.
"Sometimes, when you introduce Prolog in an organization, people will dismiss the language because they have never heard of anyone who uses it. Yet, a third of all airline tickets is handled by systems that run SICStus Prolog. NASA uses SICStus Prolog for a voice-controlled system onboard the International Space Station. Windows NT used an embedded Prolog interpreter for network configuration. New Zealand's dominant stock broking system is written in Prolog and CHR. Prolog is used to reason about business grants in Austria."
Some other notable real projects using Prolog are TerminusDB, the PLWM tiling window manager, GeneXus (which is a kind of a low-code platform that generated software from your requirements before LLMs were a thing), the TextRazor scriptable text-mining API. I think this should give you a good idea of what "Prolog-shaped" problems look like in the real world.
Others have more complete answers, but the value for me of learning Prolog (in college) was being awakened to a refreshingly different way of expressing a program. Instead of saying "do this and this and this", you say "here's what it would mean for the program to be done".
At work, I bridged the gap between task tracking software and mandatory reports (compliance, etc.). Essentially, it handles distributing the effective working hours of workers across projects, according to a varied and very detailed set of constraints (people take time off, leave the company and come back, sick days, different holidays for different remote workers, folks work on multiple stuff at the same time, have gaps in task tracking, etc.).
In the words of a colleague responsible for said reports it 'eliminated the need for 50+ people to fill timesheets, saves 15 min x 50 people x 52 weeks per year'
It has been (and still is) in use for 10+years already. I'd say 90% of the current team members don't even know the team used to have to "punch a clock" or fill timesheets way back.
Any kind of problem involving the construction, search or traversal of graphs of any variety from cyclic semi-directed graphs to trees, linear programming, constraint solving, compilers, databases, formal verification of any kind not just theorem proving, computational theory, data manipulation, and in general anything.
It looks like that is in reference to the embedded interactive code blocks. If you use uBlock Origin you can use the element picker to remove the annoying image.
Python wins out in the versatility conversation because of its ecosystem, I'm still kinda convinced that the language itself is mid.
Prolog has many implementations and you don't have the same wealth of libraries, but yes, it's Turing complete and not of the "Turing tarpit" variety, you could reasonably write entire applications in SWI-Prolog.
Right, Python is usually the second-best choice for a language for any problem --- arguably the one thing it is best at is learning to program (in Python) --- it wins based on ease-of-learning/familiarity/widespread usage/library availability.
FWIK; You can't compare the two. Python is far more general and larger than Prolog which is more specialized. However there have been various extensions to Prolog to make it more general. See Extensions section in Prolog wikipedia page - https://www.researchgate.net/publication/220281904_The_Appli...
In theory, it's as versatile as Python et al[0] but if you're using it for, e.g., serving bog-standard static pages over HTTP, you're very much using an industrial power hammer to apply screws to glass - you can probably make it work but people will look at you funny.
[0] Modulo that Python et al almost certainly have order(s) of magnitude more external libraries etc.
It's a language that should have just been a library. There's nothing noteworthy about it and it's implementable in any working language. Sometimes quite neatly. Schelog is a famous example.
Do you mean Northern Conservative Baptist Great Lakes Region Council of 1879 standard Prolog?[2]
SWI Prolog (specifically, see [2] again) is a high level interpreted language implemented in C, with an FFI to use libraries written in C[1], shipping with a standard library for HTTP, threading, ODBC, desktop GUI, and so on. In that sense it's very close to Python. You can do everyday ordinary things with it, like compute stuff, take input and output, serve HTML pages, process data. It starts up quickly, and is decently performant within its peers of high level GC languages - not v8 fast but not classic Java sluggish.
In other senses, it's not. The normal Algol-derivative things you are used to (arithmetic, text, loops) are clunky and weird. It's got the same problem as other declarative languages - writing what you want is not as easy as it seemed like it was going to be, and performance involves contorting your code into forms that the interpreter/compiler is good with.
It's got the problems of functional languages - everything must be recursion. Having to pass the whole world state in and out of things. Immutable variables and datastructures are not great for performance. Not great for naming either, temporary variable names all over.
It's got some features I've never seen in other languages - the way the constraint logic engine just works with normal variables is cool. Code-is-data-is-code is cool. Code/data is metaprogrammable in a LISP macro sort of way. New operators are just another predicate. Declarative Grammars are pretty unique.
The way the interpreter will try to find any valid path through your code - the thing which makes it so great for "write a little code, find a solution" - makes it tough to debug why things aren't working. And hard to name things, code doesn't do things it describes the relation of states to each other. That's hard to name on its own, but it's worse when you have to pass the world state and the temporary state through a load of recursive calls and try to name that clearly, too.
It's a recursive countdown. There's no deliberate typos in it, but it won't work. The reason why is subtle - that code is doing something you can't do as easily in Python. It's passing a Prolog source code expression of X-1 into the recursive call, not the result of evaluating X-1 at runtime. That's how easy metaprogramming and code-generation is! That's why it's a fun language! That's also how easy it is to trip over "the basics" you expect from other languages.
It's full of legacy, even more than Python is. It has a global state - the Prolog database - but it's shunned. It has two or three different ways of thinking about strings, and it has atoms. ISO Prolog doesn't have modules, but different implementations of Prolog do have different implementations of modules. Literals for hashtables are contentious (see [2] again). Same for object orientation, standard library predicates, and more.
And for some cases it's easier to understand if you write the backtracking yourself, and can edit/debug it. That is in case you write readable code professionally, as such algorhythms are not very intuitive for a person who sees it first time.
You might have forgotten the language but I bet it must have had some influence on how you think or write programs today. I don’t think the value of learning Prolog is necessarily that you can then write programs in Prolog, but that it shifts your perspective and adds another dimension to how you approach problems. At least this is what it has done for me and I find that still valuable today.
It means that timonoko doesn't like to think and would rather ask grok to think for them and post weird comments about it here on HN. They've been doing this for a while.
I am once again shilling the idea that someone should find a way to glue Prolog and LLMs together for better reasoning agents.
https://news.ycombinator.com/context?id=43948657
Thesis:
1. LLMs are bad at counting the number of r's in strawberry.
2. LLMs are good at writing code that counts letters in a string.
3. LLMs are bad at solving reasoning problems.
4. Prolog is good at solving reasoning problems.
5. ???
6. LLMs are good at writing prolog that solves reasoning problems.
Common replies:
1. The bitter lesson.
2. There are better solvers, ex. Z3.
3. Someone smart must have already tried and ruled it out.
Successful experiments:
1. https://quantumprolog.sgml.net/llm-demo/part1.html
> "4. Prolog is good at solving reasoning problems."
Plain Prolog's way of solving reasoning problems is effectively:
You hard code some options, write a logical condition with placeholders, and Prolog brute-forces every option in every placeholder. It doesn't do reasoning.
Arguably it lets a human express reasoning problems better than other languages by letting you write high level code in a declarative way, instead of allocating memory and choosing data types and initializing linked lists and so on, so you can focus on the reasoning, but that is no benefit to an LLM which can output any language as easily as any other. And that might have been nice compared to Pascal in 1975, it's not so different to modern garbage collected high level scripting languages. Arguably Python or JavaScript will benefit an LLM most because there are so many training examples inside it, compared to almost any other langauge.
>> You hard code some options, write a logical condition with placeholders, and Prolog brute-forces every option in every placeholder. It doesn't do reasoning.
SLD-Resolution with unification (Prolog's automated theorem proving algorithm) is the polar opposite of brute force: as the proof proceeds, the cardinality of the set of possible answers [1] decreases monotonically. Unification itself is nothing but a dirty hack to avoid having to ground the Herbrand base of a predicate before completing a proof; which is basically going from an NP-complete problem to a linear-time one (on average).
Besides which I find it very difficult to see how a language with an automated theorem prover for an interpreter "doesn't do reasoning". If automated theorem proving is not reasoning, what is?
___________________
[1] More precisely, the resolution closure.
6 replies →
Prolog was introduced to capture natural language - in a logic/symbolic way that didn't prove as powerful as today's LLM for sure, but this still means there is a large corpus of direct English to Prolog mappings available for training, and also the mapping rules are much more straightforward by design. You can pretty much translate simple sentences 1:1 into Prolog clauses as in the classic boring example
This is being taken advantage of in Prolog code generation using LLMs. In the Quantum Prolog example, the LLM is also instructed not to generate search strategies/algorithms but just planning domain representation and action clauses for changing those domain state clauses which is natural enough in vanilla Prolog.
The results are quite a bit more powerful, close to end user problems, and upward in the food chain compared to the usual LLM coding tasks for Python and JavaScript such as boilerplate code generation and similarly idiosyncratic problems.
1 reply →
Its a Horn clause resolver...that's exactly the kind of reasoning that LLMs are bad at. I have no idea how to graft Prolog to an LLM but if you can graft any programming language to it, you can graft Prolog more easily.
Also, that you push Python and JavaScript makes me think you don't know many languages. Those are terrible languages to try to graft to anything. Just because you only know those 2 languages doesn't make them good choices for something like this. Learn a real language Physicist.
11 replies →
This sparked a really fascinating discussion, I don't know if anyone will see this but thanks everyone for sharing your thoughts :)
I understand your point - to an LLM there's no meaningful difference between once turing complete language and another. I'll concede that I don't have a counter argument, and perhaps it doesn't need to be prolog - though my hunch is that LLM's tend to give better results when using purpose built tools for a given type of problem.
The only loose end I want to address is the idea of "doing reasoning."
This isn't an AGI proposal (I was careful to say "good at writing prolog") just an augmentation that (as a user) I haven't yet seen applied in practice. But neither have I seen it convincingly dismissed.
The idea is the LLM would act like an NLP parser that gradually populates a prolog ontology, like building a logic jail one brick at a time.
The result would be a living breathing knowledge base which constrains and informs the LLM's outputs.
The punchline is that I don't even know any prolog myself, I just think it's a neat idea.
Of course it does "reasoning", what do you think reasoning is? From a quick google: "the action of thinking about something in a logical, sensible way". Prolog searches through a space of logical proposition (constraints) and finds conditions that lead to solutions (if one exists).
(a) Trying adding another 100 or 1000 interlocking proposition to your problem. It will find solutions or tell you one doesn't exist. (b) You can verify the solutions yourself. You don't get that with imperative descriptions of problems. (b) Good luck sandboxing Python or JavaScript with the treat of prompt injection still unsolved.
5 replies →
Contrary to what everyone else is saying, I think you're completely correct. Using it for AI or "reasoning" is a hopeless dead end, even if people wish otherwise. However I've found that Prolog is an excellent language for expressing certain types of problems in a very concise way, like parsers, compilers, and assemblers (and many more). The whole concept of using a predicate in different modes is actually very useful in a pragmatic way for a lot of problems.
When you add in the constraint solving extensions (CLP(Z) and CLP(B) and so on) it becomes even more powerful, since you can essentially mix vanilla Prolog code with solver tools.
15 replies →
Even in your example (which is obviously not correct representation of prolog), that code will work X orders magnitude faster and with 100% reliability compared to much more inferior LLM reasoning capabilities.
3 replies →
What makes you think your brain isn't also brute forcing potential solutions subconciously and only surfacing the useful results?
29 replies →
Everything you've written here is an invalid over-reduction, I presume because you aren't terribly well versed with Prolog. Your simplification is not only outright erroneous in a few places, but essentially excludes every single facet of Prolog that makes it a turing complete logic language. What you are essentially presenting Prolog as would be like presenting C as a language where all you can do is perform operations on constants, not even being able to define functions or preprocessor macros. To assert that's what C is would be completely and obviously ludicrous, but not so many people are familiar enough with Prolog or its underlying formalisms to call you out on this.
Firstly, we must set one thing straight: Prolog definitionally does reasoning. Formal reasoning. This isn't debatable, it's a simple fact. It implements resolution (a computationally friendly inference rule over computationally-friendly logical clauses) that's sound and refutation complete, and made practical through unification. Your example is not even remotely close to how Prolog actually works, and excludes much of the extra-logical aspects that Prolog implements. Stripping it of any of this effectively changes the language beyond recognition.
> Plain Prolog's way of solving reasoning problems is effectively:
No. There is no cognate to what you wrote anywhere in how Prolog works. What you have here doesn't even qualify as a forward chaining system, though that's what it's closest to given it's somewhat how top-down systems work with their ruleset. For it to even approach a weaker forward chaining system like CLIPS, that would have to be a list of rules which require arbitrary computation and may mutate the list of rules it's operating on. A simple iteration over a list testing for conditions doesn't even remotely cut it, and again that's still not Prolog even if we switch to a top-down approach by enabling tabling.
> You hard code some options
A Prolog knowledgebase is not hardcoded.
> write a logical condition with placeholders
A horn clause is not a "logical condition", and those "placeholders" are just normal variables.
> and Prolog brute-forces every option in every placeholder.
Absolutely not. It traverses a graph proving things, and when it cannot prove something it backtracks and tries a different route, or otherwise fails. This is of course without getting into impure Prolog, or the extra-logical aspects it implements. It's a fundamentally different foundation of computation which is entirely geared towards formal reasoning.
> And that might have been nice compared to Pascal in 1975, it's not so different to modern garbage collected high level scripting languages.
It is extremely different, and the only reason you believe this is because you don't understand Prolog in the slightest, as indicated by the unsoundness of essentially everything you wrote. Prolog is as different from something like Javascript as a neural network with memory is.
4 replies →
IIRC IBM’s Watson (the one that played Jeopardy) used primitive NLP (imagine!) to form a tree of factual relations and then passed this tree to construct Prolog queries that would produce an answer to a question. One could imagine that by swapping out the NLP part with an LLM, the model would have 1. a more thorough factual basis against which to write Prolog queries and 2. a better understanding of the queries it should write to get at answers (for instance, it may exploit more tenuous relations between facts than primitive NLP).
Not so "primitive" NLP. Watson started with what its team called a "shallow parse" of a sentence using a dependency grammar and then matched the parse to an ontology consisting of good, old fashioned frames [1]. That's not as "advanced" as an LLM but far more reliable.
I believe the ontology was indeed implemented in Prolog but I forget the architecture details.
______________
[1] https://en.wikipedia.org/wiki/Frame_(artificial_intelligence...
Please tell me that's approximately what Palantir Ontology is, because if it isn't, I've no idea what it could be.
1 reply →
We've done this, and it works. Our setup is to have some agents that synthesize Prolog and other types of symbolic and/or probabilistic models. We then use these models to increase our confidence in LLM reasoning and iterate if there is some mismatch. Making synthesis work reliably on a massive set of queries is tricky, though.
Imagine a medical doctor or a lawyer. At the end of the day, their entire reasoning process can be abstracted into some probabilistic logic program which they synthesize on-the-fly using prior knowledge, access to their domain-specific literature, and observed case evidence.
There is a growing body of publications exploring various aspects of synthesis, e.g. references included in [1] are a good starting point.
[1] https://proceedings.neurips.cc/paper_files/paper/2024/file/8...
The next step is can in solve the Wicked Problems
https://en.wikipedia.org/wiki/Wicked_problem
I am once again shilling the idea that someone should find a way to glue Prolog and LLMs together for better reasoning agents.
There are definitely people researching ideas here. For my own part, I've been doing a lot of work with Jason[1], a very Prolog like logic language / agent environment with an eye towards how to integrate that with LLMs (and "other").
Nothing specific / exciting to share yet, but just thought I'd point out that there are people out there who see potential value in this sort of thing and are investigating it.
[1]: https://github.com/jason-lang/jason
Related: LLMs trained on "A is B" fail to learn "B is A"
https://arxiv.org/abs/2309.12288
You might find Eugene Asahara's detailed Prolog in the LLM Era series of about a dozen blog posts very useful - https://eugeneasahara.com/category/prolog-in-the-llm-era/
Prolog doesn't look like javascript or python so:
1. web devs are scared of it.
2. not enough training data?
I do remember having to wrestle to get prolog to do what I wanted but I haven't written any in ~10 years.
>>Prolog doesn't look like javascript or python so:
Think of this way. In Python and Javascript you write code, and to test if its correct you write unit test cases.
A prolog program is basically a bunch of test cases/unit test cases, you write it, and then tell the Prolog compiler, 'write code, that passes these test cases'.
That is, you are writing the program specification, or tests that if pass would represent solution to the problem. The job of the compiler to write the code that passes these test cases.
It's been a while since I have done web dev, but web devs back then were certainly not scared of any language. Web devs are like the ultimate polyglots. Or at least they were. I was regularly bouncing around between a half dozen languages when I was doing pro web dev. It was web devs who popularized numerous different languages to begin with simply because delivering apps through a browser allowed us a wide variety of options.
6 replies →
This is my own recent attempt at this:
https://news.ycombinator.com/item?id=45937480
The core idea of DeepClause is to use a custom Prolog-based DSL together with a metainterpreter implemented in Prolog that can keep track of execution state and implicitly manage conversational memory for an LLM. The DSL itself comes with special predicates that are interpreted by an LLM. "Vague" parts of the reasoning chain can thus be handed off to a (reasonably) advanced LLM.
Would love to collect some feedback and interesting ideas for possible applications.
As someone who did deep learning research 2017-2023, I agree. "Neurosymbolic AI" seems very obvious, but funding has just been getting tighter and more restrictive towards the direction of figuring out things that can be done with LLMs. It's like we collectively forgot that there's more than just txt2txt in the world.
YES! I've run a few experiments on classical logic problems and an LLM can spit out Prolog programs to solve the puzzel. Try it yourself, ask an LLM to write some prolog to solve some problem and then copy paste it to https://swish.swi-prolog.org/ and see if it runs.
Wouldn’t that be like a special case of neuro-symbolic programming?! There are plenty of research going on
I think prolog is the right format to codify expertise in Claude Skills. I just haven’t tested it yet.
> LLMs are bad at counting the number of r's in strawberry.
This is a tokenization issue, not an LLM issue.
Can't find the links right now, but there were some papers on llm generating prolog facts and queries to ground the reasoning part. Somebody else might have them around.
There's a lot of work in this area. See e.g., the LoRP paper by Di et al. There's also a decent amount of work on the other side too, i.e., using LLMs to convert Prolog reasoning chains back into natural language.
I think that's what these guys are doing
https://www.symbolica.ai/
If you are looking for AGI. And you understand what is going on inside of it - then it is obviously not AGI.
There are people working on integration deep learning with symbolic AI (but I don't know more)
@goblinqueen, you around?
@YeGoblynQueenne Dunno if it will ping the person
1 reply →
I've been thinking a lot about this, and I want to build the following experiment, in case anyone is interested:
The experiment is about putting an LLM to play plman[0] with and without prolog help.
plman is a pacman like game for learning prolog, it was written by profesor Francisco J. Gallego from Alicante University to teach logic subject in computer science.
Basically you write solution in prolog for a map, and plman executes it step by step so you can see visually the pacman (plman) moving around the maze eating and avoiding ghost and other traps.
There is an interesting dynamic about finding keys for doors and timing based traps.
There are different levels of complexity, and you can also write easily your maps, since they are just ascii characters in a text file.
I though this was the perfect project to visually explain my coworkers the limit of LLM "reasoning" and what is symbolic reasoning.
So far I hooked ChatGPT API to try to solve scenarios, and it fails even with substancial amount of retries. That's what I was expecting.
The next thing would be to write a mcp tool so that the LLM can navigate the problem by using the tool, but here is where I need guidance.
I'm not sure about the best dynamic to prove the usefulness of prolog in a way that goes beyond what context retrieval or db query could do.
I'm not sure if the LLM should write the prolog solution. I want to avoid to build something trivial like the LLM asking for the steps, already solved, so my intuition is telling me that I need some sort of virtual joystick mcp to hide prolog from the LLM, so the LLM could have access to the current state of the screen, and questions like what would be my position if I move up ? What's the position of the ghost in next move ? where is the door relative to my current position ?
I don't have academic background to design this experiment properly. Would be great if anyone is interested to work together on this, or give me some advice.
Prior work pending on my reading list:
- LoRP: LLM-based Logical Reasoning via Prolog [1]
- A Pipeline of Neural-Symbolic Integration to Enhance Spatial Reasoning in Large Language Models [2]
- [0] https://github.com/Matematicas1UA/plman/blob/master/README.m...
- [1] https://www.sciencedirect.com/science/article/abs/pii/S09507...
- [2] https://arxiv.org/html/2411.18564v1
yes
Prolog really is such a fantastic system, if I can justify its usage then I won't hesitate to do so. Most of the time I'll call a language that I find to be powerful a "power tool", but that doesn't apply here. Prolog is beyond a power tool. A one-off bit of experimental tech built by the greatest minds of a forgotten generation. You'd it find deep in irradiated ruins of a dead city, buried far underground in a bunker easily missed. A supercomputer with the REPL's cursor flickering away in monochrome phosphor. It's sitting there, forgotten. Dutifully waiting for you to jack in.
When I entered university for my Bachelors, I was 28 years old and already worked for 5 or 6 years as a self-taught programmer in the industry. In the first semester, we had a Logic Programming class and it was solely taught in Prolog. At first, I was mega overwhelmed. It was so different than anything I did before and I had to unlearn a lot of things that I was used to in "regular" programming. At the end of the class, I was a convert! It also opened up my mind to functional programming and mathematical/logical thinking in general.
I still think that Prolog should be mandatory for every programmer. It opens up the mind in such a logical way... Love it.
Unfortunately, I never found an opportunity in my 11 years since then to use it in my professional practice. Or maybe I just missed the opportunities?????
Did they teach you how to use DCGs? A few months ago I used EDCGs as part of a de-spaghettification and bug fixing effort to trawl a really nasty 10k loc sepples compilation unit and generate tags for different parts of it. Think ending up with a couple thousand ground terms like:
tag(TypeOfTag, ParentFunction, Line).
Type of tag indicating things like an unnecessary function call, unidiomatic conditional, etc.
I then used the REPL to pull things apart, wrote some manual notes, and then consulted my complete knowledgebase to create an action plan. Pretty classical expert system stuff. Originally I was expecting the bug fixing effort to take a couple of months. 10 days of Prolog code + 2 days of Prolog interaction + 3 days of sepples weedwacking and adjusting the what remained in the plugboard.
1 reply →
Prolog is a great language to learn. But I wouldn't want to use it for anything more than what its directly good at. Especially the cut operator, that's pretty mind bending. But once you get good at it, it all just flows. But I doubt more than 1% of devs could ever master it, even on an unlimited timeline. Its just much harder than any other type of non-research dev work.
In university, Learning prolog was my first encounter with the idea that my IQ may not be as high as I thought
I also found it mindbending.
But some parts, like e.g. the cut operator is something I've copied several times over for various things. A couple of prototype parser generators for example - allowing backtracking, but using a cut to indicate when backtracking is an error can be quite helpful.
"Keep your exclamation points under control. You are allowed no more than two or three per 100,000 words of prose."
Elmore Leonard, on writing. But he might as well have been talking about the cut operator.
At uni I had assignments where we were simply not allowed to use it.
2 replies →
I thoroughly enjoyed doing all the exercises. It was challenging and hence, fun!
I don't think I ever learned how it can be useful other than feeding the mind.
There was a time when the thinking was you can load all the facts into a prolog engine and it would replace experts like doctors and engineers - expert systems, it didn't work. Now its a curiosity
intro to quantum physics for me (which is only sophomore) I noped out of advanced math/physics at that point, luckily I did learn to code on my own
I had more success with the Prolog language track on https://exercism.org/tracks/prolog
It's a mind-bending language and if you want to experience the feeling of learning programming from the beginning again this would be it
My prolog anecdote: ~2001 my brother and I writing an A* pathfinder in prolog to navigate a bot around the world of Asheron's Call (still the greatest MMORPG of all time!). A formative experience in what can be done with code. Others had written a plugin system (called Decal) in C for the game and a parser library for the game's terrain file format. We took that data and used prolog to write an A* pathfinder that could navigate the world, avoiding un-walkable terrain and even using the portals to shortcut between locations. Good times.
Two Prolog books that I find very interesting:
Advanced Turbo prolog - https://archive.org/details/advancedturbopro0000schi/mode/2u...
Prolog programming for artificial intelligence - https://archive.org/details/prologprogrammin0000brat_l1m9/mo...
There seems to an interesting difference between Prolog and conventional (predicate) logic.
In Prolog, anything that can't be inferred from the knowledge base is false. If nothing about "playsAirGuitar(mia)" is implied by the knowledge base, it's false. All the facts are assumed to be given; therefore, if something isn't given, it must be false.
Predicate logic is the opposite: If I can't infer anything about "playsAirGuitar(mia)" from my axioms, it might be true or false. It's truth value is unknown. It's true in some model of the axioms, and false in others. The statement is independent of the axioms.
Deductive logic assumes an open universe, Prolog a closed universe.
This is called the Closed World Assumption (CWA) - https://en.wikipedia.org/wiki/Closed-world_assumption
Prolog’s Closed-World Assumption: A Journey Through Time - https://medium.com/@kenichisasagawa/prologs-closed-world-ass...
Is Prolog really based on the closed-world assumption? - https://stackoverflow.com/questions/65014705/is-prolog-reall...
It's not really false I think. It's 'no', which is an answer to a question "Do I know this to be true?"
I think there should be a room for three values there: true, unprovable, false. Where false things are also unprovable. I wonder if Prolog has false, defined as "yes" of the opposite.
> It's not really false I think. It's 'no', which is an answer to a question "Do I know this to be true?"
I don't think so, because in this case both x and not-x could be "no", but I think in Prolog, if x is "no", not-x is "yes", even if neither is known to be true. It's not a three-valued logic that doesn't adhere to the law of the excluded middle.
3 replies →
I recently implemented an eagerly evaluated embedded Prolog dialect in Dart for my game applications. I used SWI documentation extensively to figure out what to implement.
But I think I had the most difficulty designing the interface between the logic code and Dart. I ended up with a way to add "Dart-defined relations", where you provide relations backed dynamically by your ECS or database. State stays in imperative land, rules stay in logic land.
Testing on Queens8, SWI is about 10,000 times faster than my implementation. It's a work of art! But it doesn't have the ease of use in my game dev context as a simple Dart library does.
Would you mind sharing your prolog dart lib?
Unfortunately it is closed source. My plan is to use it as (part of) the foundation for my game studio.
Do you have a different use case? I would be open to sharing it on a project- or time-limited basis in exchange for bug reports and feature requests.
I've recently started modeling some of my domains/potential code designs in Prolog. I'm not that advanced. I don't really know Prolog that well. But even just using a couple basic prolog patterns to implement a working spec in the 'prolog way' is *unbelievably* useful for shipping really clean code designs to replace hoary old chestnut code. (prolog -> ruby)
I keep wishing for "regex for prolog", ie: being able to (in an arbitrary language) express some functional bits in "prolog-ish", and then be able to ask/query against it.
(not exactly that, but hopefully you get the gist)
There's so much stuff regarding constraints, access control, relationship queries that could be expressed "simply" in prolog and being able to extract out those interior buts for further use in your more traditional programming language would be really helpful! (...at least in my imagination ;-)
You can do that in Racket, with the Racklog library¹. There's also Datalog² and MiniKanren and probably some other logic languages available.
[1] https://docs.racket-lang.org/racklog/index.html
[2] https://docs.racket-lang.org/datalog/index.html
While usually using native syntax rather than strings, somethign like that exists for most languages of any popularity (and many obscure ones), in the form of miniKanren implementations.
https://minikanren.org/
1 reply →
There are a bunch of libraries that will do this - here's one example of a python one: https://github.com/yuce/pyswip - and a ruby one: https://github.com/preston/ruby-prolog
2 replies →
You might be interested in Flix:
https://play.flix.dev/?q=PQgECUFMBcFcCcB2BnUBDUBjA9gG15JtAJb...
is an embedded Datalog DB and query in a general-purpose programming language.
More examples on https://flix.dev/
You could try picat
[0] https://picat-lang.org/
What you mean is not "regex for Prolog" but an embedded PROLOG interpreter, which exists.
Ironically, the most common way I have seen people do this is use an embedded LISP interpreter, in which a small PROLOG is easily implemented.
https://www.metalevel.at/lisprolog/ suggests Lisprolog (Here are some embedded LISPs: ECL, PicoLisp, tulisp)
SWI-Prolog can also be linked against C/C++ code: https://stackoverflow.com/questions/65118493/is-there-any-re... https://sourceforge.net/p/gprolog/code/ci/457f7b447c2b9e90a0...
Racklog is an embedded PROLOG for Racket (Scheme): https://docs.racket-lang.org/racklog/
I've wished for the same kind of 'embed prolog in my ruby' for enumerating all possible cases, all invalid cases, etc in test suites. Interesting to know it's not just me!
4 replies →
I wonder if there's examples of whole product architectures done in Prolog, seems like an elegant solution if done right. I've been looking for a concise way to model full architectures of my various projects, without relying on having a typical markdown file.
Which is separate from the actual types in the code.
Which is separate from the deployment section of the docs.
I studied prolog back in 2014. It was used in AI course. I found it very confusing: trying to code A*, N-Queens, or anything in it was just too much. Python, in contrast, was a god-send. I failed the subject twice in my MSc (luckily passing the MSc was based on the total average), but did a similar course in UC Berkeley, with python: aced it, loved it, and learned a lot.
Never again :D
A similar thing happened at my university in an Advanced Algorithms course. Students failed it so much, the university was forced to make the course easier to pass, by removing the minimum grade to pass.
I believe your case (and many other students) is that you couldn't abstract yourself from imperative programming (python) into logic programming (prolog).
It's a query language for graph database. You can write A* and N-Queens in SQL, but why?
Performance, far better performance. Same reason you ever use SQL. Prolog can do the same thing for very specific problems.
PS Prolog is a Horn clause solver. You characterizing it as a query language for a graph database, well it doesn't put you in the best light. It makes it seem like you don't understand important foundational CS math concepts.
2 replies →
I remember writing a Prolog(ish) interpreter in Common Lisp in an 90's AI course in grad school for Theorem proving (which is essentially what Prolog is doing under the hood). Really foundational to my understanding of how declarative programming works. In an ideal world I would still be programming in Lisp and using Prolog tools.
> In an ideal world…
I see this sentiment a lot lately. A sense of missed nostalgia.
What happened?
In 20 years, will people reminisce about JavaScript frameworks and reminisce how this was an ideal world??
Speaking as someone who just started exploring Prolog and lisp, and ended up in the frozen north isolated from internet - access. The tools were initially locked/commercial only during a critical period, and then everyone was oriented around GUIs - and GUI environments were very hostile to the historical tools, and thus provided a different kind of access barrier.
A side one is that the LISP ecology in the 80s was hostile to "working well with others" and wanted to have their entire ecosystem in their own image files. (which, btw, is one of the same reasons I'm wary of Rust cough)
Really, it's only become open once more with the rise of WASM, systemic efficiency of computers, and open source tools finally being pretty solid.
I can tell you, from the year 2045, that running the worlds global economy on Javascript was the direct link to the annihilation of most of our freedom and existence. Hope this helps.
1 reply →
It is not nostalgia. It is mathematical thought. It is more akin to to an equation and more provably correct. Closer to fundamental truth -- like touching fundamental reality.
I also see the CL or Tcl+C or Assembly as an ideal world.
I only read the first 88 pages of Prolog Programming in Depth but I found it to be the best introductory book for programming in Prolog because it presents down to earth examples of coding like e.g. reading a file, storing data. Most other books are mainly or only focused on the pure logic stuff of Prolog but when you program you need more.
Another way of getting stuff done would be to use another programming language with its standard library (with regex, networking, json, ...) and embed or call Prolog code for the pure logic stuff.
I remember a project I did in undergrad with Prolog that would fit connecting parts of theoretical widgets together based on constraints about how different pieces could connect and it just worked instantly and it felt like magic because I had absolutely no clue how I would have coded that in Pascal or COBOL at that time. It blew my mind because the program was so simple.
Prolog is easily one of my favorite languages, and as many others in this thread, I first encountered it during university. I ended up teaching it for a couple of years (along with Haskell) and ever since, I've gone on an involuntary prolog bender of sorts once or twice a year. I almost always use it for Advent of code as well.
Hah. Found this book back at my dad's this past winter: https://imgur.com/a/CyG1E2P
Had never heard of it before, and this is first I'm hearing of it since.
Also had other cool old shit, like CIB copies of Borland Turbo Pascal 6.0, old Maxis games, Windows 3.1
Clocksin is the standard Prolog textbook used in universities. I studied from the 5th edition.
Nice. Do you use Prolog much today? If so, when do you reach for it?
1 reply →
Declarative languages are fantastic to reason about code.
But the true power is unlocked once the underlying libraries are implemented in a way that surpassesthe performance that a human can achieve.
Since implementation details are hidden, caches and parallelism can be added without the programmer noticing anything else than a performance increase.
This is why SQL has received a boost the last decade with massively parallel implementations such as BigQuery, Trino and to some extent DuckDB. And what about adding a CUDA backend?
But all this comes at a cost and needs to be planned so it is only used when needed.
Look into Futhark, its a pure FP language (based on ML, ick) that outputs CUDA.
I'll never understand how it's a programming language not a graph database with query language. It's more MongoDb than Fortran.
Because its more powerful than MongoDb or Fortran. The cut operator for instance gives it the ability to express things you just can't do in those other systems. The trade-off is that mastering the cut operator is a rare skill and only that one person who can do it can maintain the Prolog code. Compare that with MongoDb where even the village idiot can use it but with a huge performance cost.
I don't know about MongoDB and its query language, but wrt Fortran, it's unreasonable to say that Prolog is more powerful than Fortran (or vice versa). A more reasonable statement is that Prolog is more expressive than Fortran (though this gets fuzzy, we have to define expressiveness in a way that lets us rank languages). But the power of a language normally means what we can compute using that language. Prolog and Fortran both have the same level of "power", but it's certainly fair to say that expressing many programs is easier in Prolog than Fortran, and there are some (thinking back to my scientific computing days) that are easier to express in Fortran than Prolog.
5 replies →
There's also another difference. MongoDb and Fortran served a purpose.
Previously:
Learn Prolog Now - https://news.ycombinator.com/item?id=1976127 - Dec 2010 (31 comments)
How many Prolog programmers does it take to change a lightbulb?
No.
Always felt this would be language that Sherlock Holmes would use...so be sure to wear the hat when learning it
“A touch! A distinct touch!” cried Holmes. "You are developing a certain unexpected vein of pawky humour, Watson, against which I must learn to guard myself".
-- from "The Valley of Fear" by Arthur Conan Doyle.
That's it, I'm convinced. Now I'm doing my next startup in Prolog.
Thats the spirit. Make sure someone else is paying.
There are declarative languages like SQL and XSLT.
And then there are declarative languages like Prolog.
I recently asked @grok about Prolog being useless incomprehensible shit for anything bigger than one page:
Professionals write Prolog by focusing on the predicates and relations and leaving the execution flow to the interpreter. They also use the Constraint Logic Programming extensions (like clpfd) which use smart, external algorithms to solve problems instead of relying on Prolog's relatively "dumb" brute-force search, which is what typically leads to the "exploding brain" effect in complex code.
--- Worth mentioning here is that I wrote Prolog all on my own in 1979. On top of Nokolisp of course. There was no other functioning Prolog at that time I knew about.
Thereafter I have often planned "Infinity-Prolog" which can solve impossible problem with lazy evaluation.
I just learned from @grok that this Constraint Logic is basically what was aiming at.
How many times are you going to "write" the same comment in one discussion?
I really enjoyed learning Prolog in university, but it is a weird language. I think that 98% of tasks I would not want to use Prolog for, but for that remaining 2% of tasks it's extremely well suited for. I have always wished that I could easily call Prolog easily from other languages when it suited the use case, however good luck getting most companies to allow writing some code in Prolog.
That is where Lisp or Scheme weirdly shines. It is incredibly easy to add prolog to a Lisp or a Scheme. It’s almost as if it comes out naturally if you just go down the rabbit hole.
“The little prover” is a fantastic book for that. The whole series is.
I worked through the little scheme but not the little prover, I think Ill take a look at that. Thanks.
1 reply →
Racket really shines in this regard: Racket makes it easy to build little DSLs, but they all play perfectly together because the underlying data model is the same. Example from the Racket home page: https://racket-lang.org/#any-syntax
You can have a module written in the `#racket` language (i.e., regular Racket) and then a separate module written in `#datalog` and the two can talk to each other!
[dead]
But I don't wanna!
I love Prolog, and have seen so many interesting use cases for it.
In the end though, it mostly just feels enough of a separate universe to any other language or ecosystem I'm using for projects that there's a clear threshold for bringing it in.
If there was a really strong prolog implementation with a great community and ecosystem around, in say Python or Go, that would be killer. I know there are some implementations, but the ones I've looked into seem to be either not very full-blown in their Prolog support, or have close to non-existent usage.
What kind of problems is Prolog helping to solve besides GOFAI, theorem proving and computational linguistics?
Here's a page with some examples of use-cases that fit Prolog well: https://www.metalevel.at/prolog/business
"Sometimes, when you introduce Prolog in an organization, people will dismiss the language because they have never heard of anyone who uses it. Yet, a third of all airline tickets is handled by systems that run SICStus Prolog. NASA uses SICStus Prolog for a voice-controlled system onboard the International Space Station. Windows NT used an embedded Prolog interpreter for network configuration. New Zealand's dominant stock broking system is written in Prolog and CHR. Prolog is used to reason about business grants in Austria."
Some other notable real projects using Prolog are TerminusDB, the PLWM tiling window manager, GeneXus (which is a kind of a low-code platform that generated software from your requirements before LLMs were a thing), the TextRazor scriptable text-mining API. I think this should give you a good idea of what "Prolog-shaped" problems look like in the real world.
Others have more complete answers, but the value for me of learning Prolog (in college) was being awakened to a refreshingly different way of expressing a program. Instead of saying "do this and this and this", you say "here's what it would mean for the program to be done".
At work, I bridged the gap between task tracking software and mandatory reports (compliance, etc.). Essentially, it handles distributing the effective working hours of workers across projects, according to a varied and very detailed set of constraints (people take time off, leave the company and come back, sick days, different holidays for different remote workers, folks work on multiple stuff at the same time, have gaps in task tracking, etc.).
In the words of a colleague responsible for said reports it 'eliminated the need for 50+ people to fill timesheets, saves 15 min x 50 people x 52 weeks per year'
It has been (and still is) in use for 10+years already. I'd say 90% of the current team members don't even know the team used to have to "punch a clock" or fill timesheets way back.
Prolog's constraint solving and unification are exactly what is required for solving type-checking constraints in a Hindley-Milner type system.
Yes, absolutely...I just wish the people who wrote FP compilers knew this.
Any kind of problem involving the construction, search or traversal of graphs of any variety from cyclic semi-directed graphs to trees, linear programming, constraint solving, compilers, databases, formal verification of any kind not just theorem proving, computational theory, data manipulation, and in general anything.
See my comment here - https://news.ycombinator.com/item?id=45902960
Scheduling, relational modeling, parsing. These things come up all the time. Look at DCG:s if you want to quickly become dangerous.
The background image says "testing version" - is there a production version?
It looks like that is in reference to the embedded interactive code blocks. If you use uBlock Origin you can use the element picker to remove the annoying image.
is prolog a use-case language or is it as versatile as python?
Python wins out in the versatility conversation because of its ecosystem, I'm still kinda convinced that the language itself is mid.
Prolog has many implementations and you don't have the same wealth of libraries, but yes, it's Turing complete and not of the "Turing tarpit" variety, you could reasonably write entire applications in SWI-Prolog.
Right, Python is usually the second-best choice for a language for any problem --- arguably the one thing it is best at is learning to program (in Python) --- it wins based on ease-of-learning/familiarity/widespread usage/library availability.
4 replies →
FWIK; You can't compare the two. Python is far more general and larger than Prolog which is more specialized. However there have been various extensions to Prolog to make it more general. See Extensions section in Prolog wikipedia page - https://www.researchgate.net/publication/220281904_The_Appli...
In theory, it's as versatile as Python et al[0] but if you're using it for, e.g., serving bog-standard static pages over HTTP, you're very much using an industrial power hammer to apply screws to glass - you can probably make it work but people will look at you funny.
[0] Modulo that Python et al almost certainly have order(s) of magnitude more external libraries etc.
> you can probably make it work but people will look at you funny
Don't threaten me with a good time
It's a language that should have just been a library. There's nothing noteworthy about it and it's implementable in any working language. Sometimes quite neatly. Schelog is a famous example.
That's like comparing a nuclear reactor to a pickup truck. They are different things and one doesn't replace the other in any meaningful way.
Do you mean Northern Conservative Baptist Great Lakes Region Council of 1879 standard Prolog?[2]
SWI Prolog (specifically, see [2] again) is a high level interpreted language implemented in C, with an FFI to use libraries written in C[1], shipping with a standard library for HTTP, threading, ODBC, desktop GUI, and so on. In that sense it's very close to Python. You can do everyday ordinary things with it, like compute stuff, take input and output, serve HTML pages, process data. It starts up quickly, and is decently performant within its peers of high level GC languages - not v8 fast but not classic Java sluggish.
In other senses, it's not. The normal Algol-derivative things you are used to (arithmetic, text, loops) are clunky and weird. It's got the same problem as other declarative languages - writing what you want is not as easy as it seemed like it was going to be, and performance involves contorting your code into forms that the interpreter/compiler is good with.
It's got the problems of functional languages - everything must be recursion. Having to pass the whole world state in and out of things. Immutable variables and datastructures are not great for performance. Not great for naming either, temporary variable names all over.
It's got some features I've never seen in other languages - the way the constraint logic engine just works with normal variables is cool. Code-is-data-is-code is cool. Code/data is metaprogrammable in a LISP macro sort of way. New operators are just another predicate. Declarative Grammars are pretty unique.
The way the interpreter will try to find any valid path through your code - the thing which makes it so great for "write a little code, find a solution" - makes it tough to debug why things aren't working. And hard to name things, code doesn't do things it describes the relation of states to each other. That's hard to name on its own, but it's worse when you have to pass the world state and the temporary state through a load of recursive calls and try to name that clearly, too.
This is fun:
It's a recursive countdown. There's no deliberate typos in it, but it won't work. The reason why is subtle - that code is doing something you can't do as easily in Python. It's passing a Prolog source code expression of X-1 into the recursive call, not the result of evaluating X-1 at runtime. That's how easy metaprogramming and code-generation is! That's why it's a fun language! That's also how easy it is to trip over "the basics" you expect from other languages.
It's full of legacy, even more than Python is. It has a global state - the Prolog database - but it's shunned. It has two or three different ways of thinking about strings, and it has atoms. ISO Prolog doesn't have modules, but different implementations of Prolog do have different implementations of modules. Literals for hashtables are contentious (see [2] again). Same for object orientation, standard library predicates, and more.
[1] https://news.ycombinator.com/item?id=26624442
Learn Datalog Now!
Is there a WebAssembly WASI version of swi prolog ?
Not sure... Other Prologs compiled to WASM with very good performance is https://ciao-lang.org/playground/
The same toplevel runs also from 'node' as well.
Thanks. I will have a look. I would like to integrate one Prolog in exaequOS.
We had it in university courses and it seemed useless. DSL for backtracking.
Yes. As an add-on or library, it could be useful, but as a language it's just a forgotten dead end.
And for some cases it's easier to understand if you write the backtracking yourself, and can edit/debug it. That is in case you write readable code professionally, as such algorhythms are not very intuitive for a person who sees it first time.
Learn it now? I learned back in the 80s... and have since forgotten
You might have forgotten the language but I bet it must have had some influence on how you think or write programs today. I don’t think the value of learning Prolog is necessarily that you can then write programs in Prolog, but that it shifts your perspective and adds another dimension to how you approach problems. At least this is what it has done for me and I find that still valuable today.
yes
[flagged]
Farts:
what the fuck does this mean?
are you saying you can’t comprehend prolog programs?
It means that timonoko doesn't like to think and would rather ask grok to think for them and post weird comments about it here on HN. They've been doing this for a while.