[0] https://www.complang.tuwien.ac.at/ulrich/prolog_misc/acomip....
There's TUI that allows querying of issues and the resource hierarchy. Issues can also be output as JSON which is fed into a LLM to produce cleanup actions and management reporting.
The Prolog app is very fast, considering what it's doing, largely because it makes heavy use of tabling so once an issue has been detected it's not recomputed when queries are made.
[1] https://web.archive.org/web/20190525163234/https://dev.to/da...
I don't think I've ever felt like it's OK for my program to provide a list of answers where some are right and some are wrong, but reading this... and generally believing in P != NP.... maybe that's a decent way of looking at some stuff!
Returning 3 was "wrong", but infinitely more correct than retuning 0.
For those with familiarity with both Prolog and Erlang, can you comment on the similarities and differences between? Is/was Erlang basically Prolog with OTP bolted on?
% Prolog
next_light(green, yellow).
next_light(yellow, red).
next_light(red, green).
% Erlang
next_light(green) -> yellow;
next_light(yellow) -> red;
next_light(red) -> green.
- `;` in Erlang indicates multi clause function
- In Prolog next_light is database, in Erlang it's just a function
Question: What's the light before green?
% Erlang
% Returns actual value
prev_light_search() ->
[State || State <- [green, yellow, red], next_light(State) == green].
% Prolog (?- means that it's in query mode)
?- next_light(Light, green).
In a nutshell Erlang is more like: "when I have X, then I can calculate Y" and Prolog like "If I want Y, what's the X".
1) Read the JSON with Prolog (there's a library) and assertz() the facts from that, building an immutable database in the first phase before applying the rules in the second phase.
2) Externally transform the JSON into Prolog facts, load that into the app on startup and apply the same rules to it.
I agree that mutating the database in the second phase is probably a bad idea, but that's not the same as saying "assertz() always bad". I'd read his site before it appeared on HN and whilst there a lot of very good stuff on it, some of it reminds me of FP purist edicts - fine if you want to go that way and it's appropriate to your problem, but that isn't always going to be the case. That was the basis of my earlier (downvoted) "Mostly overblown" comment.
But nice to see Prolog mentioned at all on HN :-)
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The version without ! looks identical to the version with ! except only the ! is removed - is this a joke?
import solver
solver.add_variable(x)
solver.variable_range(x, 0, 100)
solver.add_constraint(x, greater_than, 50)
solver.solve_for(x)
:- using constraint solver library
X in 0..100,
X #> 50,
label(X)
> "and what ! does"
This is a concept which doesn't translate easily to other languages, but analogously it's like the performance difference between this code which always searches the entire haystack:
found = false
for item in haystack:
if item == 'needle':
found = true
return found
for item in haystack:
if item == 'needle':
return true
return false
[1] They aren't Prolog "functions", they are predicates, functions are different, but it will do for this explanation.
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Radio Yerevan: A listener asks: "Is it true that in Moscow, on Red Square, they are giving away cars?"
Our answer: "Yes, it is true. Except it isn't in Moscow, but in Leningrad. And it isn't on Red Square, but on Palace Square. And they aren't cars, but bicycles. And they aren't giving them away, they are stealing them."
I'm fine with all that. The language gives you sensible tools to deal with edge cases that otherwise require you to jump through hoops or import libraries (like the constraint arithmetic libraries that Markus recommends... and that he had to mostly write himself before he could recommend). You can get into a spot of bother if you use those facilities without knowing why they are there and why you shouldn't just spam them in every case, but that's why good textbooks exist.
And more to the point, that's why Prolog Coding Guidelines are a thing, more precisely, a paper, which you can find here from the website of Michael Covington who's one of its (many) authors:
https://www.covingtoninnovations.com/mc/plcoding.pdf
Here's what the Guidelines has to say about assert/retract:
5.10 Avoid asserta/assertz and retract unless you actually need to preserve information through backtracking Although it depends on your compiler, asserta/assertz and retract are usually very slow. Their purpose is to store information that must survive backtracking. If you are merely passing intermediate results from one step of computation to the next, use arguments.
If you have a dynamic predicate, write interface predicates for changing it instead of using “bare” calls to asserta/assertz and retract, so that your interface predicates can check that the changes are logically correct, maintain mutexes for multiple threads, and so forth.
Sound advice. In fact that's what I've always done myself even before reading the Guidelines.
And here's some advice on using the "horror" of the cut without having to wake the Great Old Ones:
5.4 Use cuts sparingly but precisely First think through how to do the computation without a cut; then add cuts to save work. For further guidance see O’Keefe (1990, pp. 88–101). Concerning code layout, make sure cuts do not go unnoticed: if a green cut7 may be placed on the same line as the previous predicate call, red cuts definitely must be on their own line of code.
5.5 Never add a cut to correct an unknown problem A common type of Prolog programing error is manifested in a predicate that yields the right result on the first try but goes wrong upon backtracking. Rather than add a cut to eliminate the backtracking, investigate what went wrong with the logic. There is a real risk that if the problem is cured by adding the cut, the cut will be far away from the actual error (even in a different predicate), which will remain present to cause other problems later
Basically the message should be that we can help the novice to navigate the complexities of the language without underestimating or pataronising them. Cuts, asserts, and the lot, are just there to make things easier. They only make things harder when they're not explained properly. And telling everyone to just stay away from them is, I think, not the proper way to explain anything.
I have a application that has actions and actions can happen pretty much in any order. By default all scenarios should be an error except for ones that are in the boundary of logical steps, e.g.
login -> ...[!logout]... -> logout
login -> ...[!logout]... -> dashboard
upload -> ... -> view_file
So I have ~50 actions, ~30 constraints which generate >200 scenarios which then are transformed into test suite.
Yet in short: Prolog is useful everywhere it's simple to express a rule but not that easy to implement it.
Users simply had to change the basic "facts" (who was available on what days, how many people were needed), and the program solved for the various constraints and offered solutions.
It was maybe about 300 lines of Prolog, no complex dependencies. It replaced a pile of Python scripts that required a lot of state, didn't really work, and could only run on a few specific computers.
For regular users, it was relatively easy to understand and change the facts. SWIPL for web also offers a nice "notebook" interface that lets you mix data, code, and markdown / output blocks so the documentation was inline.
Prolog also works extremely well as a target language for code generation by LLMs for these domains due to it being "higher up in the food chain" compared to procedural languages so to speak, and because Prolog was originally envisioned for classic NLP and hence has a corpus of one-to-one mappings from natural language to logic (as in the example in [3]). So well in fact that even with last-gen models textual descriptions for suitable problems become the bottleneck and you can in many cases just go straight to Prolog code instead ([2]).
[1]: https://quantumprolog.sgml.net
This loops Prolog, but terminates in Datalog:
p :- p.
p.
?- p.
This is because the underlying mechanism is completely different. Datalog is like SQL with recursion, you start with known facts and repeatedly applies rules to derive all consequences until nothing new appears. In Prolog, you start from the query and works backward through rules until it either finds a proof or fails.
So, Datalog treats Horn clauses as database constraints/inference rules while Prolog treats Horn clauses as a search program. They use the same mathematical substrate, but completely different computational models.
- Datalog is a syntactic subset of Prolog
- Real Prolog implementations generally have the ability to configure their runtimes such that it becomes a proper superset of Datalog
[0] https://github.com/flix/flix
[1] https://github.com/rust-lang/polonius
[2] RDFox
[3] https://github.com/eclipse-biscuit/biscuit
[4] https://github.com/vmware-archive/differential-datalog [5] https://github.com/brurucy/pydbsp
https://v3.yarnpkg.com/features/constraints
It never got released for good though. I actually had need of such feature for a project but I thought that using an exoteric programming language and an experimental feature was a bit much. I ended up setting up those constraints as a CI check hand-made script and the code was surprisingly large (~300 lines), but not that hard to understand.
Here[0] is an example of using Ruby and Prolog to solve a real-world AWS management problem.
0 - https://web.archive.org/web/20190525163234/https://dev.to/da...
(This is also the problem with "I'll just quickly implement a Prolog-like DSL when I need it". Sometimes not a bad idea, but you have to be realistic. Your "lightweight" Prolog will be worse in every way compared to serious Prolog implementations).
Prolog also has exceptionally good string management through DCG:s. If you have a bunch of structured text and want to query it, Prolog allows you to do that. The syntax is very simple so you can even just sed your data into Prolog code and load that sometimes.
Supposedly SICSTUS Prolog is used in flight booking, and NASA did something with it sometime, but that's kind of obscure. If you ever need a rule engine, consider poking it with logic programming for a bit, perhaps it'll turn out both pragmatic and elegant.
https://t3x.org/lisp64k/prolog.html
The linked code runs in this: Unix, DOS and CP/M
https://t3x.org/klisp/index.html
It will compile for CP/M and DOS (Turbo C), Unix, Windows and whatnot.
64k Lisp:
https://t3x.org/lisp64k/index.html
Zenlisp it's similar but almost gives an intro CS course (pre SICP) for the cheap implementing discrete Math and tons of stuff in pure Lisp, even rational and complex numbers. The end chapter it's about logic programmer of course.
Like most "Prolog implemented in a LISP" examples, that's not Prolog. It's a Prolog-like LISP language that is missing the point. Implementing Prolog in Lisp is only "trivial" if you try to do something trivial and call it "implementing Prolog in LISLP", instead of actually, you know, implementing Prolog. In Lisp.
Since we're discussing an article by Markus Triska, and he's commented on the same thing, I'll let him do the honours:
https://github.com/triska/lisprolog
Some online books show how to implement simple "Prolog" engines in Lisp. These engines typically assume a representation of Prolog programs that is convenient from a Lisp perspective, and can't even parse a single proper Prolog term. Instead, they require you to manually translate Prolog programs to Lisp forms that are no longer valid Prolog syntax. With this approach, implementing a simple "Lisp" in Prolog is even easier ("Lisp in Prolog in zero lines"): Manually translate each Lisp function to a Prolog predicate with one additional argument to hold the original function's return value. Done. This is possible since a function is a special case of a relation, and functional programming is a restricted form of logic programming.
I'll just add that this idea of a "trivial" implementation of Prolog in Lisp seems to come from SICP which itself proudly shows off how to "trivially" do the job. I once sat through a couple of hours of lecturing on Logic Programming and Prolog by Gerald Sussman, on youtube, just to try and form a mental model of his mental model about Prolog. I waited, and waited, and waited in vain for the other shoe to drop, thinking "well he must be coming to the subject of Resolution theorem-proving any moment now" but he never did. Instead he presented a version of Prolog that is quite unrecognisable by any working Prolog programmer, from the syntax, right down to the semantics; a version that only seemed to loosely share ideas like backtracking and pattern matching with Prolog. One-sided pattern-matching mind, not unification, because unification is, for some reason, really scary to Lisp and functional programming folks. A "Prolog" with the syntax of Scheme and with the semantics of Lisp variables and primitives like eval and so on. And when he was finally asked "how is real Prolog different" at the end of the lecture he said "because Prolog programmers have a really efficient interpreter". Meaning, I guess, the Warren Abstract Machine. Very disappointing.
1. I used GoLog (a Prolog interpreted in Go) for defining some functional tests in the project where the testing infrastructure was otherwise written in Go.
2. I used Prolog (SWI) to write a parser for Thrift (both the definition and binary format) simply because I needed one, and Prolog was convenient.
What I expect people who use Prolog for the stuff it's really good at is databases that encode some complicated business or legal processes. I.e. databases with many complex constraints that have to all somehow come together to produce a solution set. Prolog would also be a good language to encode / query graph databases. So, whatever you can think of being a good match for a graph database would also work well with Prolog.
There are also (even more niche) Prolog derivatives, eg. Ciao or Mercury, that are... well, decent all-purpose languages. You can just use them in the same context where you'd use Python or Haskell respectively. The implementations are pretty solid in terms of performance and correctness... so, if you like the approach, then why not?
Where in imperative programming languages you supply arguments to a function and get a result in Prolog you can give the result of a function call and get all argument sets that lead to that result.
But you are right that in production systems you would seldom see Prolog, for reason that you can easily LLM.
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Understanding is a personal achievement and has nothing to do with "forbidden knowledge" when the source of said knowledge is both quoted above and freely available.
Of course this is far from the best way to do this.
If so, the language they thought they were using (and that they should actually use) is datalog, not prolog.
Datalog has declarative semantics: All facts that are derivable from the base database and the rules will be derived by the interpreter, and it will not add extra hallucinated facts. If that's not true, it's a bug in the runtime, not in the language.
So it's not that they "discovered" anything about Prolog; they already knew the language inside out.
This article explains how to appropriately use Prolog declaratively and with full generality.
Early versions of Erlang were implemented in Prolog, which is why Erlang's syntax looks a whole lot like Prolog's, but beyond that they're not very similar.
I guess what may also have contributed is that there are a number of concurrent logics implemented in Prolog for prototyping Erlang's scheduler such as Concurrent Transaction Logic ([1]).
is/2 is arithmetic evaluator. It runs only in one direction and it does not solve equations.
#>, #=, etc. are constraints, like (in)equalities over linear arithmetic. When constraints have the form of some known theory (like in SMT solvers), they can be solved (incrementally). That is called "constraint logic programming" (CLP). Modern Prolog systems are indeed CLP systems.
Prolog is older than CLP. CLP is older than SMT. Prolog+CLP systems are turing complete, can be used as programming languages. SMT is powerful but not a programming language.
Can "impure" features be avoided? Not in all cases. Think of them as 'unsafe' in Rust, but less dangerous.
Markus pushes for more purity in Prolog (using CLPFD), but sometimes some impurity (or imperative-like code with side-effects) is the best solution. Sometimes the pure solution is also the better. In other cases, it is not. Better compilers and static analyzers can reduce the friction between these worlds.
Take away: do pure code if you can afford it and it looks like a natural solution to your problem, use impure features later if you really need them.
https://www.swi-prolog.org/pldoc/man?section=clpfd-integer-a...
That's just showing getting rid of the cut in two stages. The line that makes it possible to remove it is this one:
N #> 0,
Markus Triskas' argument is that if you use the #> etc versions of declarative arithmetic operators, instead of the > ones, you can then call the factorial predicate with both arguments as variables, i.e. without inputs, just outputs, to enumerate the entire factorial relation. Like this:
?- n_factorial(N, F).
N = 0, F = 1
; N = 1, F = 1
; N = 2, F = 2
; N = 3, F = 6
; ... .
Of course there are other ways to write a factorial predicate (or any predicate) so that it always enumerates a relation but they are more verbose. Then again, you do need a special library to use declarative arithmetic anyway, so.
Over the years I've come to a similar position in other languages as well. If a functional-ish solution fits performance constraints and is maintainable, don't mutate or reach for global state, things like that.
But you can use it for lots of things. Whenever I'm frustrated with graph based tools being slow (like build systems), I run the graph through a datalog engine for comparison. It's usually much, much faster.