The Power of Prolog

https://gist.github.com/calroc/603ed919bc814ccee10c1b3df6142...

    There are five houses.
    The Englishman lives in the red house.
    The Spaniard owns the dog.
    Coffee is drunk in the green house.
    The Ukrainian drinks tea.
    The green house is immediately to the right of the ivory house.
    The Old Gold smoker owns snails.
    Kools are smoked in the yellow house.
    Milk is drunk in the middle house.
    The Norwegian lives in the first house.
    The man who smokes Chesterfields lives in the house next to the man with the fox.
    Kools are smoked in the house next to the house where the horse is kept.
    The Lucky Strike smoker drinks orange juice.
    The Japanese smokes Parliaments.
    The Norwegian lives next to the blue house.

In the interest of clarity, it must be added that each of the five houses is painted a different color, and their inhabitants are of different national extractions, own different pets, drink different beverages and smoke different brands of American cigarets [sic]. One other thing: in statement 6, right means your right.

    :- use_module(library(clpfd)).

    house_next_to(H0, H1) :- abs(H0 - H1) #= 1.

    puzzle([
		GreenHouse, RedHouse, IvoryHouse, YellowHouse, BlueHouse,
		Englishman, Spaniard, Ukrainian, Norwegian, Japanese,
		Dog, Snails, Fox, Horse, Zebra,
		Coffee, Tea, Milk, OrangeJuice, Water,
		OldGold, Kools, Chesterfields, LuckyStrike, Parliaments
		]) :-

		Vars = [GreenHouse, RedHouse, IvoryHouse, YellowHouse, BlueHouse,
			Englishman, Spaniard, Ukrainian, Norwegian, Japanese,
			Dog, Snails, Fox, Horse, Zebra,
			Coffee, Tea, Milk, OrangeJuice, Water,
			OldGold, Kools, Chesterfields, LuckyStrike, Parliaments
		],
		
		Vars ins 1..5,
		
		all_distinct([GreenHouse, RedHouse, IvoryHouse, YellowHouse, BlueHouse]),
		all_distinct([Englishman, Spaniard, Ukrainian, Norwegian, Japanese]),
		all_distinct([Dog, Snails, Fox, Horse, Zebra]),
		all_distinct([Coffee, Tea, Milk, OrangeJuice, Water]),
		all_distinct([OldGold, Kools, Chesterfields, LuckyStrike, Parliaments]),
		
		Englishman #= RedHouse,
		Spaniard #= Dog,
		Coffee #= GreenHouse,
		Ukrainian #= Tea,
		GreenHouse #= 1 + IvoryHouse,
		OldGold #= Snails,
		Kools #= YellowHouse,
		Milk #= 3,
		Norwegian #= 1,
		house_next_to(Chesterfields, Fox),
		house_next_to(Kools, Horse),
		LuckyStrike #= OrangeJuice,
		Japanese #= Parliaments,
		house_next_to(Norwegian, BlueHouse).

    puzzle([
		GreenHouse, RedHouse, IvoryHouse, YellowHouse, BlueHouse,
		Englishman, Spaniard, Ukrainian, Norwegian, Japanese,
		Dog, Snails, Fox, Horse, Zebra,
		Coffee, Tea, Milk, OrangeJuice, Water,
		OldGold, Kools, Chesterfields, LuckyStrike, Parliaments]),
	label([
		GreenHouse, RedHouse, IvoryHouse, YellowHouse, BlueHouse,
		Englishman, Spaniard, Ukrainian, Norwegian, Japanese,
		Dog, Snails, Fox, Horse, Zebra,
		Coffee, Tea, Milk, OrangeJuice, Water,
		OldGold, Kools, Chesterfields, LuckyStrike, Parliaments]).

I have only two small suggestion: First, since you know that Vars = [GreenHouse, ...] (the Prolog program states this as a constraint), you can simply substitute Vars every time for this list. Therefore, you can start the whole program with: puzzle(Vars) :- ...

Second, exactly the same reasoning applies for the query you show. You can write it as: ?- puzzle(Vs), label(Vs).

Thank you very much for posting this beautiful solution, and also for carefully clarifying the additional assumptions!

The way I know it, PGMs and HMMs are firmly within GOFAI- they're basically graphs with weights. Although I guess you can say the same thing about ANNs, the primary purpose of PGMs and HMMs is to represent structure (specifically, dependency relations). Which is a very GOFAI thing, to my mind.

It really depends on where you put the distinction between GOFAI and something else. My understanding is that the commonly accepted split is between AI with a (symbolic) representation and AI without, and the original proponent of the latter is Rodney Brooks who coined the term "Nouvelle AI" (sometimes "Nouveau") that is usually used as the modern counterpart to GOFAI.

Personally, I believe the distinction is a bit stale, especially if you think that ANNs are actually older than expert systems and that even if you look just a couple of years in the past you'll see that one of the most popular (family of) machine learning algorithms was Decision Tree learners, which are basically a perfect mix of the old algorithms and the new: a propositional logic rule-base built from examples using information-theoretic measures.

As to the revolution brought by Hinton, this is a bit political but I think that's just a good story, with little grounding in reality. Here's an example:

https://webcache.googleusercontent.com/search?q=cache:2JwVBr...

That's a (cached) page from an online machine learning data repository, with data sets taken from various papers etc. The page is about a familial relationship data set from two papers, one by Hinton and one by Quinlan (the Decision Tree guy). Hinton's paper is from 1986, Quinlan's from 1989. Hinton's paper has 43 citations, all but one of which is from 2002 or earlier and going back to the '90s (Quinlan's only has one, from 1990).

Which tells me that Hinton's "breakthrough result" did not revive interest in ANNs, neither was he the tenacious, lone underdog, that he likes to present thimself as: interest in ANNs and specifically Hinton's work has always been very popular, as has been the work of others (er, Schmidhuber).

My understanding is that machine learning goes way back, and basically starts with GOFAI: the first thing people noticed about expert systems was the difficulty of hand-crafting non-trivial rule-bases, so they looked for ways to automate that (see, e.g. Michalski's work that goes back to the '60s) and eventually realised that you didn't actually need the expert system if you could just automate the rule extraction.

But that's also probably just a cute but inaccurate narrative- there was a great deal of work in machine learning and a great deal of interest in it, that was not labelled as such, for instance, in "inductive inference" and so on.

tl;dr: when we talk of machine learning and AI these days we're hardly even scratching the surface. There's a lot of past work that most university courses simply don't have the time to teach and we newer students are missing lots of useful bits of it... and are probably condemned to reinvent.