the-power-of-prolog VS livebook

Prolog is excellent for bikeshedding, in fact that might be its strongest axis. It starts with everything you get in a normal language such as naming things, indentation, functional purity vs side effects, where to break code into different files and builds on that with having your names try to make sense in declarative, relational, logical and imperative contexts, having your predicates (functions) usable in all modes - and then performant in all modes - having your code be deterministic, and then deterministic in all modes. Being 50 years old there are five decades of learning "idiomatic Prolog" ideas to choose from, and five decades of footguns pointing at your two feet; it has tabling, label(l)ing, SLD and SLG resolution to choose from. Built in constraint solvers are excellent at tempting you into thinking your problem will be well solved by the constraint solvers (it won't be, you idiot, why did you think that was a constraint problem?), two different kinds of arithmetic - one which works but is bad and one which mostly works on integers but clashes with the Prolog solver - and enough metaprogramming that you can build castles in the sky which are very hard to debug instead of real castles. But wait, there's more! Declarative context grammars let you add the fun of left-recursive parsing problems to all your tasks, while attributed variables allow the Prolog engine to break your code behind the scenes in new and interesting ways, plenty of special syntax not to be sneezed at (-->; [_|[]] {}\[]>>() \X^+() =.. #<==> atchoo (bless you)), a delightful deep-rooted schism between text as linked lists of character codes or text as linked lists of character atoms, and always the ISO-Standard-Sword of Damocles hanging over your head as you look at the vast array of slightly-incompatible implementations with no widely accepted CPython-like-dominant-default.

Somewhere hiding in there is a language with enough flexibility and metaprogramming to let your meat brain stretch as far as you want, enough cyborg attachments to augment you beyond plain human, enough spells and rituals to conjour tentacled seamonsters with excellent logic ability from the cold Atlantic deeps to intimidate your problem into submission.

Which you, dear programmer, can learn to wield up to the advanced level of a toddler in a machine shop in a mere couple of handfuls of long years! Expertise may take a few lifetimes longer - in the meantime have you noticed your code isn't pure, doesn't work in all modes, isn't performant in several modes, isn't using the preferred idiom style, is non-deterministic, can't be used to generate as well as test, falls into a left-recursive endless search after the first result, isn't compatible with other Prolog Systems, and your predicates are poorly named and you use the builtin database which is temptingly convenient but absolutely verboten? Plenty for you to be getting on with, back to the drawing boar...bikeshed with you.

And, cut! No, don't cut; OK, green cuts but not red cuts and I hope you aren't colourblind. Next up, coroutines, freeze, PEngines, and the second 90%.

Visit https://www.metalevel.at/prolog and marvel as a master deftly disecting problems, in the same way you marvel at Peter Norvig's Pytudes https://github.com/norvig/pytudes , and sob as the wonders turn to clay in your ordinary hands. Luckily it has a squeaky little brute force searcher, dutifully headbutting every wall as it explores all the corners of your problem on its eventual way to an answer, which you can always rely on. And with that it's almost like any other high level mostly-interpreted dynamic programming / scripting language.

There is also AI that isn't machine learning. One could use formal logic to state rules and facts about the world and infer things from that. This sounds attractive but the main issue is that you need to build and maintain all of this knowledge. Most oldschool AI falls into this category. There's also fun programming languages like Prolog that are deep into this school: https://www.metalevel.at/prolog

There's a nice book[1][2] about Prolog, with modern characteristics. Moreover, there are things like ProbLog[3] and DeepProbLog[4] that allow you to use probabilistic reasoning and power of machine learning. I am personally looking forward for Scryer Prolog[5] to achieve its goals.

[1] https://www.metalevel.at/prolog

[2] https://github.com/triska/the-power-of-prolog

[3] https://github.com/ML-KULeuven/problog

[4] https://github.com/ML-KULeuven/deepproblog

[5] https://github.com/mthom/scryer-prolog

I had the same troubles until I encountered Markus Triska's modern perspective on revitalizing Prolog: https://www.metalevel.at/prolog.

The Power of Prolog [0] is a fantastic blog/video series covering everything from basic syntax, theoretical basis, modern features and idiomatic constructs.

I highly recommend it if you want to get the gist of Prolog and its modern features.

If you want a tour of Prolog, you can watch the video with that name [1].

[0]: https://www.metalevel.at/prolog

[1]: https://youtu.be/8XUutFBbUrg

I've gone through The Art of Prolog, most of The Power of Prolog, and a good chunk of the P-99 problems, and I have to say I'm simultaneously fascinated by and sceptical of Prolog. For some problem domains, implicit search is a very desirable property, and I can definitely see Prolog shining in that case. There are also many desirable properties and possibilities that are often reiterated, but concrete examples of how they would work are often missing. It comes down to: how does "production Prolog" look? A talk on Strange Loop by Michael Hendricks on exactly that topic was really helpful (especially w.r.t. some useful tools and libraries: func and yall are really great, and I still need to check mavis), but it still leaves me wondering on a couple of things.

Pretty much every Prolog book is quite good, but if you have the money or a local library with a copy, I really like Programming in Prolog by Clocksin, or Art of Prolog by Stering and Shapiro. If you want to follow a web resource, the standard suggestion is Markus Triska's The Power of Prolog.

To get started you need a running instance of Livebook

LiveBook site

I prefer functional programming with Livebook[1] for this type of thing. Once you run a cell, it can be published right into a web component as well.

[1] - https://livebook.dev

If you're a coder, Livebook might be worth a look too. I certainly have my eyes on it.

Would highly recommend looking at Jose's use of livebook to answer these. It makes testing easier. It's old but still relevant. Video link inside

That's hard to say. Racket is a rather complete language, as is F# and Elixir. And F# and Racket are extremely capable multi-paradigm languages, supporting basically any paradigm. Elixir is a bit more restricted in terms of its paradigms, but that's a feature oftentimes, and it also makes up for it with its process framework and deep VM support from the BEAM.

I would say that the key difference is that F# and Elixir are backed by industry whereas Racket is primarily backed via academia. Thus, the incentives and goals are more aligned for F# and Elixir to be used in industrial settings.

Also, both F# and Elixir gain a lot from their host VMs in the CLR and BEAM. Overall, F# is the cleanest language of the three, as it is easy to write concise imperative, functional, or OOP code and has easy asynchronous facilities. Elixir supports macros, and although Racket's macro system is far more advanced, I don't think it really provides any measurable utility over Elixir's. I would also say that F# and Elixir's documentation is better than Racket's. Racket has a lot of documentation, but it can be a little terse at times. And Elixir definitely has the most active, vibrant, and complete ecosystem of all three languages, as well as job market.

The last thing is that F# and Elixir have extremely good notebook implementations in Polyglot Notebooks (https://marketplace.visualstudio.com/items?itemName=ms-dotne...) and Livebook (https://livebook.dev/), respectively. I would say both of these exceed the standard Python Jupyter notebook, and Racket doesn't have anything like Polyglot Notebooks or Livebook. (As an aside, it's possible for someone to implement a Racket kernel for Polyglot Notebooks, so maybe that's a good side project for me.)

So for me, over time, it has slowly whittled down to F# and Elixir being my two languages that I reach for to handle effectively any project. Racket just doesn't pull me in that direction, and I would say that Racket is a bit too locked to DrRacket. I tried doing some GUI stuff in Racket, and despite it having an already built framework, I have actually found it easier to write my own due to bugs found and the poor performance of Racket Draw.

This looks very similar to LiveBook¹. It is purely Elixir/BEAM based, but is quite polished and seems like a perfect workflow tool that is also able to expose these workflows (simply called livebooks) as web apps that some functional, non-technical person can execute on his/her own.

1: https://livebook.dev/