I second the recommendation in Sir Whinesalot's post (which I haven't fully read yet) to look at miniKanren and microKanren. I found it extremely educational to port microKanren to OCaml a few years ago, and I think the result is somewhat more comprehensible than the original Scheme, though you'll still probably have to read the paper to understand it: http://canonical.org/~kragen/sw/dev3/mukanren.ml
The most astonishing result of miniKanren is a Scheme interpreter that you can run forwards, backwards, or both. http://webyrd.net/quines/quines.pdf demonstrates using it to generate a Scheme quine, that is, a Scheme program whose output when run is itself ("miniKanren, Live and Untagged:
Quine Generation via Relational Interpreters
(Programming Pearl)", Byrd, Holk, and Friedman).
Unlike the post, I don't think Datalog is the place to look for deep insights about logic programming. Instead, it's the place to look for deep insights about databases.
fcatalan · 58m ago
I concur that SICP 4.4 is very approachable. I once took a class that had a simple Prolog assignment, I recall we were given some building plans and had to program a path solver through the building.
I thought it was a bit too easy and I wanted to dig deeper, because just doing the task left you with a taste of "this is magic, just use these spells".
I looked at how to implement Prolog and was stumped until I found that SICP section.
So I ported it to JavaScript and gave it a Prolog-like syntax and made a web page where you could run the assignment but also exposed the inner workings, and it was one of the neatest things I've ever handed in as coursework.
ashton314 · 6h ago
I did a detailed write-up of implementing miniKanren here:
By the end of it, I implement a small type checker that, when you run it backwards (by giving the checker a type), it proceeds to enumerate programs that inhabit that type!
kragen · 5h ago
Isn't that amazing‽ I wonder if you could guide its search with an LLM...
ashton314 · 5h ago
There is some research work I’m aware of that’s trying to make type-safe LLM generation a thing.
philzook · 5h ago
Nice!
I'll note there is a really shallow version of naive datalog I rather like if you're willing to compromise on syntax and nonlinear variable use.
edge = {(1,2), (2,3)}
path = set()
for i in range(10):
# path(x,y) :- edge(x,y).
path |= edge
# path(x,z) :- edge(x,y), path(y,z).
path |= {(x,z) for x,y in edge for (y1,z) in path if y == y1}
Similarly it's pretty easy to hand write SQL in a style that looks similar and gain a lot of functionality and performance from stock database engines. https://www.philipzucker.com/tiny-sqlite-datalog/
Thank you! I have been searching for something like this but for some reason couldn't find your work.
I am currently implementing a Datalog to PostgreSQL query engine at work as we want to experiment with modeling authorization rules in Datalog and then run authorization queries directly in the database. As I want to minimize the round trips to the database I use a different approach than yours and translate Datalog programs to recursive CTEs. These are a bit limited, so we have to restrict ourselves to linearly recursive Datalog programs, but for the purpose of modeling authorization rules that seems to be enough (e.g. you can still model things such as "permissions propagate from groups to group members").
kragen · 5h ago
That's exciting!
xlii · 7h ago
Lately I’ve been dabbling with different Prolog implementations and Constraint Handling Rules which led me to CLIPS [0] (in Public Domain, but developed at NASA - sounds neat doesn’t it?)
It’s not very easy to get into, but it’s very fast on rule resolution and being pure C is easy to integrate. I’m trying to get smart code parsing using logic language and this seems promising. I’m also a Lisp nerd so that works for me :)
This window manager implemented in Prolog popped up here recently. It's really cool!
I jumped to it as a new daily driver in the hope that I'd learn some Prolog, and it's been quite the success, actually. The developer is really nice, and he's generously helped me with some basic questions and small PRs.
Definitely recommended. I have a Guix package for it if anyone's interested.
Any reading recommendations for high quality logic programming codebases?
michae2 · 3h ago
Something I’ve wondered about Datalog is whether integers can be added to the language without losing guarantees about termination of query evaluation. It seems like as soon as we add integers with successor() or strings with concat() then we can potentially create infinite relations. Is there a way to add integers or strings (well, really basic scalar operations on integer or string values) while preserving termination guarantees?
This bit at the end of the article seems to imply it’s possible, maybe with some tricks?
> We could also add support for arithmetic and composite atoms (like lists), which introduce some challenges if we wish to stay “Turing-incomplete”.
ulrikrasmussen · 2h ago
Not without a termination checker. Take a look at Twelf, it is a logic programming language and proof assistant based on the dependently typed LF logical framework. You can use general algebraic types in relations, and in general queries can be non-terminating. However, the system has a fairly simple way of checking termination using moded relations and checking that recursive calls have structurally smaller terms in all arguments.
Twelf is quite elegant, although not as powerful as other proof assistants such as Coq. Proofs in Twelf are simply logic programs which have been checked to be total and terminating.
The syntax of Twelf is a bit different from other logic languages, but just note that every rule must have a name and that instead of writing `head :- subgoal1, subgoal2, ..., subgoaln` you write `ruleName : head <- subgoal1 <- subgoal2 <- ... <- subgoaln`.
Also note that this approach only works for top-down evaluation because it still allows you to define infinite relations (e.g. the successor relation for natural numbers is infinite). Bottom-up evaluation will fail to terminate unless restricted to only derive facts that contribute to some ground query. I don't know if anyone have looked into that problem, but that seems interesting. It is probably related to the "magic sets" transformation for optimizing bottom-up queries, but as far as I understand that does not give any hard guarantees to performance, and I don't know how it would apply to this problem.
wduquette · 2h ago
It’s all about the terms. As soon as rules can create an infinite sequence of new terms for a single relation, e.g. by addition, you’ve got non-termination.
fogzen · 1h ago
Yes, for some kinds of operations on some kinds of data structures. The keyword/property you're looking for is "monotonicity". Monotonic functions are guaranteed to terminate under fixed-point semantics.
Look into Datafun: A total functional language that generalizes Datalog. Also be sure to watch Datafun author Michael Arntzenius's Strangeloop talk.
I love Prolog but haven't used it in ages. I should really give it a go again. Whenever I used it my brain needed some adjustment time and then switched to declarative mode. It's a really unique experience and hard to describe. It was also my first contact with immutable data.
Is implementing a Kanren and embedding it as suggested by the author really the recommended approach? Back in the day I used Sicstus mostly but tried to use SWI whenever possible (because I'm a FLOSS guy at heart). I'm asking because we went the opposite direction and used Prolog as the first language and called Java or C when needed (io, GUI). I'd describe the result as a "hot mess".
Random note: "Art of Prolog" and "Craft of Prolog" remain two of my favorite CS books to this day.
I'd be curious what the "state of the art" is these days and would love ve to hear from some folks using Prolog in the trenches.
fracus · 7h ago
I think it would be really impactful to start with a problem and describe how logic programming solves that problem better than the other paradigms.
cjonas · 7h ago
The only production experience I have with logic programming is OPA Rego for writing security policies (not sure it's a "pure" logic language but feels like the primary paradigm).
I found it pretty interesting for that use case, although the learning curve isn't trivial for traditional devs.
I've been reading a bit about it, and it seems easier to make goal-driven backwards chaining AI from it, like the block world example. You could in theory use that for something like a video game AI (like GOAP, Goal-Oriented Action Planning, which is based on STRIPS). Whenever I read about GOAP though, they seem to have used a graphical editor to declaratively input rules rather than a logic programming language.
Note that I'm not an expert in any of this, I've just been reading about this kind of AI recently. I haven't actually done this myself.
dkjaudyeqooe · 7h ago
Generally speaking, the advantage of logic programming is that it's (more) declarative: you describe the problem and it derives a solution.
taeric · 7h ago
Ish? Is only really true if what you are programming can be seen as a search for the completion of a statement?
For an easy example to consider, what would the logical program look like that described any common fractal? https://rosettacode.org/wiki/Koch_curve#Prolog shows that... it is not necessarily a win for this idea.
For the general task asked in the OP here, I would hope you could find an example in rosettacode that shows prolog gets a good implementation. Unfortunately, I get the impression some folks prefer code golf for these more so than they do "makes the problem obvious."
rabbits77 · 5h ago
I’d argue that is not the most ideal Prolog solution. More like it’s simply a recursive implementation of an imperative solution.
For fractals you’ll want to be able to recognize and generate the structures. It’s a great use case for Definite Clause Grammars (DCGs). A perfect example of this would be Triska’s Dragon Curve implementation. https://www.youtube.com/watch?v=DMdiPC1ZckI
taeric · 3h ago
I would agree. I was actually hoping to be proven wrong with that example. I have yet to see anything other than a constraint program that looks easier to understand in logic programming, sadly.
vvern · 5h ago
Some time ago I worked on cockroachdb and I was working on implementing planning for complex online schema changes.
We really wanted a model that could convincingly handle and reasonably schedule arbitrary combinations of schema change statements that are valid in Postgres. Unlike mysql postgres offers transactional schema changes. Unlike Postgres, cockroach strives to implement online schema changes in a protocol inspired by f1 [0]. Also, you want to make sure you can safely roll back (until you’ve reached the point where you know it can’t fail, then only metadata updates are allowed).
The model we came up with was to decompose all things that can possibly change into “elements” [1] and each element had a schedule of state transitions that move the element through a sequence of states from public to absent or vice versa [2]. Each state transitions has operations [3].
Anyway, you end up wanting to define rules that say that certain element states have to be entered before other if the elements are related in some way. Or perhaps some transitions should happen at the same time. To express these rules I created a little datalog-like framework I called rel [4]. This lets you embed in go a rules engine that then you can add indexes to so that you can have sufficiently efficient implementation and know that all your lookups are indexed statically. You write the rules in Go [5]. To be honest it could be more ergonomic.
The rules are written in Go but for testing and visibility they produce a datomic-inspired format [6]. There’s a lot of rules now!
The internal implementation isn’t too far off from the search implementation presented here [7]. Here’s unify [8]. The thing has some indexes and index selection for acceleration. It also has inverted indexes for set containment queries.
It was fun to make a little embedded logic language and to have had a reason to!
Or more accurately, a super simple Datalog implementation.
cartucho1 · 4h ago
Not really logic programming, but a while ago I made this, also in Python:
https://github.com/ariroffe/logics (mainly for educational purposes).
Parts of the implementation kind of reminded me of it.
The most astonishing result of miniKanren is a Scheme interpreter that you can run forwards, backwards, or both. http://webyrd.net/quines/quines.pdf demonstrates using it to generate a Scheme quine, that is, a Scheme program whose output when run is itself ("miniKanren, Live and Untagged: Quine Generation via Relational Interpreters (Programming Pearl)", Byrd, Holk, and Friedman).
§4.4 of SICP http://sarabander.github.io/sicp/html/4_002e4.xhtml also has an implementation of logic programming in Scheme which is extremely approachable.
Unlike the post, I don't think Datalog is the place to look for deep insights about logic programming. Instead, it's the place to look for deep insights about databases.
I looked at how to implement Prolog and was stumped until I found that SICP section.
So I ported it to JavaScript and gave it a Prolog-like syntax and made a web page where you could run the assignment but also exposed the inner workings, and it was one of the neatest things I've ever handed in as coursework.
https://codeberg.org/ashton314/microKanren
By the end of it, I implement a small type checker that, when you run it backwards (by giving the checker a type), it proceeds to enumerate programs that inhabit that type!
I'll note there is a really shallow version of naive datalog I rather like if you're willing to compromise on syntax and nonlinear variable use.
Similarly it's pretty easy to hand write SQL in a style that looks similar and gain a lot of functionality and performance from stock database engines. https://www.philipzucker.com/tiny-sqlite-datalog/I wrote a small datalog from the Z3 AST to sqlite recently along these lines https://github.com/philzook58/knuckledragger/blob/main/kdrag...
I am currently implementing a Datalog to PostgreSQL query engine at work as we want to experiment with modeling authorization rules in Datalog and then run authorization queries directly in the database. As I want to minimize the round trips to the database I use a different approach than yours and translate Datalog programs to recursive CTEs. These are a bit limited, so we have to restrict ourselves to linearly recursive Datalog programs, but for the purpose of modeling authorization rules that seems to be enough (e.g. you can still model things such as "permissions propagate from groups to group members").
It’s not very easy to get into, but it’s very fast on rule resolution and being pure C is easy to integrate. I’m trying to get smart code parsing using logic language and this seems promising. I’m also a Lisp nerd so that works for me :)
[0]: https://www.clipsrules.net/
This window manager implemented in Prolog popped up here recently. It's really cool!
I jumped to it as a new daily driver in the hope that I'd learn some Prolog, and it's been quite the success, actually. The developer is really nice, and he's generously helped me with some basic questions and small PRs.
Definitely recommended. I have a Guix package for it if anyone's interested.
Any reading recommendations for high quality logic programming codebases?
This bit at the end of the article seems to imply it’s possible, maybe with some tricks?
> We could also add support for arithmetic and composite atoms (like lists), which introduce some challenges if we wish to stay “Turing-incomplete”.
Twelf is quite elegant, although not as powerful as other proof assistants such as Coq. Proofs in Twelf are simply logic programs which have been checked to be total and terminating.
Edit: Here's a link to a short page in the manual which shows how termination checking works: https://twelf.org/wiki/percent-terminates/
The syntax of Twelf is a bit different from other logic languages, but just note that every rule must have a name and that instead of writing `head :- subgoal1, subgoal2, ..., subgoaln` you write `ruleName : head <- subgoal1 <- subgoal2 <- ... <- subgoaln`.
Also note that this approach only works for top-down evaluation because it still allows you to define infinite relations (e.g. the successor relation for natural numbers is infinite). Bottom-up evaluation will fail to terminate unless restricted to only derive facts that contribute to some ground query. I don't know if anyone have looked into that problem, but that seems interesting. It is probably related to the "magic sets" transformation for optimizing bottom-up queries, but as far as I understand that does not give any hard guarantees to performance, and I don't know how it would apply to this problem.
Look into Datafun: A total functional language that generalizes Datalog. Also be sure to watch Datafun author Michael Arntzenius's Strangeloop talk.
https://www.rntz.net/datafun/
https://www.youtube.com/watch?v=gC295d3V9gE
Is implementing a Kanren and embedding it as suggested by the author really the recommended approach? Back in the day I used Sicstus mostly but tried to use SWI whenever possible (because I'm a FLOSS guy at heart). I'm asking because we went the opposite direction and used Prolog as the first language and called Java or C when needed (io, GUI). I'd describe the result as a "hot mess".
Random note: "Art of Prolog" and "Craft of Prolog" remain two of my favorite CS books to this day.
I'd be curious what the "state of the art" is these days and would love ve to hear from some folks using Prolog in the trenches.
I found it pretty interesting for that use case, although the learning curve isn't trivial for traditional devs.
https://www.openpolicyagent.org/
Note that I'm not an expert in any of this, I've just been reading about this kind of AI recently. I haven't actually done this myself.
For an easy example to consider, what would the logical program look like that described any common fractal? https://rosettacode.org/wiki/Koch_curve#Prolog shows that... it is not necessarily a win for this idea.
For the general task asked in the OP here, I would hope you could find an example in rosettacode that shows prolog gets a good implementation. Unfortunately, I get the impression some folks prefer code golf for these more so than they do "makes the problem obvious."
For fractals you’ll want to be able to recognize and generate the structures. It’s a great use case for Definite Clause Grammars (DCGs). A perfect example of this would be Triska’s Dragon Curve implementation. https://www.youtube.com/watch?v=DMdiPC1ZckI
We really wanted a model that could convincingly handle and reasonably schedule arbitrary combinations of schema change statements that are valid in Postgres. Unlike mysql postgres offers transactional schema changes. Unlike Postgres, cockroach strives to implement online schema changes in a protocol inspired by f1 [0]. Also, you want to make sure you can safely roll back (until you’ve reached the point where you know it can’t fail, then only metadata updates are allowed).
The model we came up with was to decompose all things that can possibly change into “elements” [1] and each element had a schedule of state transitions that move the element through a sequence of states from public to absent or vice versa [2]. Each state transitions has operations [3].
Anyway, you end up wanting to define rules that say that certain element states have to be entered before other if the elements are related in some way. Or perhaps some transitions should happen at the same time. To express these rules I created a little datalog-like framework I called rel [4]. This lets you embed in go a rules engine that then you can add indexes to so that you can have sufficiently efficient implementation and know that all your lookups are indexed statically. You write the rules in Go [5]. To be honest it could be more ergonomic.
The rules are written in Go but for testing and visibility they produce a datomic-inspired format [6]. There’s a lot of rules now!
The internal implementation isn’t too far off from the search implementation presented here [7]. Here’s unify [8]. The thing has some indexes and index selection for acceleration. It also has inverted indexes for set containment queries.
It was fun to make a little embedded logic language and to have had a reason to!
0: https://static.googleusercontent.com/media/research.google.c... 1: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 2: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 3: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 4: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 5: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 6: https://github.com/cockroachdb/cockroach/blob/master/pkg/sql... 7: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 8: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa...