# Logic Programming

So far, substitutions have been a means to an end. Once we found the most general unifier, we discarded any work we did along the way.

This time, the substitutions take center stage. We start with our trusty mgu:

{-# LANGUAGE BlockArguments, DeriveFunctor, DeriveFoldable #-}
import Control.Arrow (second)
import Data.List (nub, transpose)
import Text.Megaparsec
import Text.Megaparsec.Char
type Parser = Parsec () String

data ExpF a = C String | V a | ExpF a :@ ExpF a deriving (Eq, Functor, Foldable, Show)
type VarId = String
type Exp = ExpF VarId
type Subst = [(VarId, Exp)];

apply :: Subst -> Exp -> Exp
apply sub t = case t of
a :@ b -> apply sub a :@ apply sub b
V v    -> maybe t id $lookup v sub _ -> t mgu :: Exp -> Exp -> Subst -> [Subst] mgu t u sub = case (apply sub t, apply sub u) of (C a, C b) | a == b -> [sub] (V a, V b) | a == b -> [sub] (V a, x) -> varBind a x (x, V a) -> varBind a x (x1 :@ y1, x2 :@ y2) -> mgu x1 x2 sub >>= mgu y1 y2 _ -> [] where varBind v t = if elem v t then [] else [(v, t):map (second$ apply [(v, t)]) sub]


In the past, we applied substitutions before calling mgu. Here, we pass in substitutions and mgu applies them on our behalf, so we can easily try out different substitutions on the same term.

Instead of a Maybe Subst we return a list of 0 or 1 substitutions.

We define a simple grammar for parsing Exp values:

fact :: Parser Exp
fact = apps where
apps = foldl1 (:@) <$> some (con <|> var <|> between (spch '(') (spch ')') apps) con = C <$> sp ((:) <$> (digitChar <|> lowerChar) <*> many alphaNumChar) var = V <$> sp ((:) <$> upperChar <*> many alphaNumChar) sp = (<* many (char ' ')) spch = sp . char mustFact :: String -> Exp mustFact = either undefined id . parse fact ""  Suppose we are told alfred is a parent of aethelflaed, and then we are asked for a parent of aethelflaed. We can answer this query via unification: exampleTrivial = mgu fact q [] where fact = mustFact "parent alfred aethelflaed" q = mustFact "parent X aethelflaed"  [[("X",C "alfred")]] Let’s build on this simple idea. ## Do you know where your children are? Given multiple facts, we can find all that unify with a given query: births :: [Exp] births = mustFact <$>
[ "parent alfred aethelflaed"
, "parent aethelflaed aelfwynn"
, "parent alfred edward"
, "parent edward aethelstan"
, "parent edward edmund"
, "parent edmund edgar"
]

exampleMultiple = concat [mgu p q [] | p <- births]
where q = mustFact "parent edward X"

[[("X",C "aethelstan")],[("X",C "edmund")],[("X",C "eadred")]]

## Grand slam

We extend our system so a predicate depends on any number of other predicates:

type Rule = [Exp]
rule :: Parser Rule
rule = (:) <$> fact <*> (option [] (sp (string ":-") *> sepBy1 fact (spch ','))) mustRule :: String -> Rule mustRule = either undefined id . parse rule "" grand :: [Rule] grand = mustRule "grandparent A B :- parent A X, parent X B" : ((:[]) <$> births)


The births are predicates which are unconditionally true because they have no dependencies, while grandparent A B is true only if we can unfiy parent A X with a true predicate and unify parent X B with a true predicate.

We find all solutions with a depth-first search:

dfs :: [Rule] -> Exp -> Subst -> [Subst]
dfs rules q sub = concat [f t =<< mgu h q sub | (h:t) <- rules]
where
f xs s' = case xs of
[]     -> [s']
(x:xt) -> f xt =<< dfs rules x s'

exampleGrand = dfs grand (mustFact "grandparent alfred Y") []

[[("Y",C "aelfwynn"),("X",C "aethelflaed"),("B",V "Y"),("A",C "alfred")],
[("Y",C "aethelstan"),("X",C "edward"),("B",V "Y"),("A",C "alfred")],
[("Y",C "edmund"),("X",C "edward"),("B",V "Y"),("A",C "alfred")],
[("Y",C "eadred"),("X",C "edward"),("B",V "Y"),("A",C "alfred")]]

## Keep it fresh

In our previous example, we had to carefully chose distinct variable names. It makes more sense for variables to be scoped to the rule in which they appear. This makes the language friendlier, and also allows recursive rules.

We’ll achieve this by automatically generating fresh variables each time we encounter a rule. But first, we break down our search for substitutions into easily digestible pieces.

The design of our code is similar to the basic parser combinators described by Swierstra, or the readS a type from the base library: a function taking a string and returning the list of possible ways to parse the beginning of the input string along with the unparsed remainder.

We define a "predicate combinator" Pred to be a function taking an Env consisting of the substitutions required so far and a stream of integers representing unused variables and returning all Env values that satisfy the predicate.

type Env = (Subst, [Int])
type Pred = Env -> [Env]

pristine :: Env
pristine = ([], [0..])


The only Pred we can build from scratch is one that attempts to unify two given expressions:

(~~) :: Exp -> Exp -> Pred
s ~~ t = $$sub, vs) -> [(s', vs) | s' <- mgu s t sub]  Otherwise we build a Pred value from existing Pred values or indirectly calling (~~). fresh :: (Exp -> Pred) -> Pred fresh f = \(sub, v:vs) -> f (V  '_':show v) (sub, vs) (\/!) :: Pred -> Pred -> Pred (\/!) x y e = x e <> y e (/\!) :: Pred -> Pred -> Pred (/\!) x y e = x e >>= y nono :: Pred -> Pred nono x e = if null  x e then [e] else []  Our depth-first search becomes: dfs' rules q = fst <> f q pristine where f q = foldr1 (\/!)  foldr1 (/\!) . zipWith () ((q ~~):repeat f) <> rules exampleGrand' = dfs' grand  mustFact "grandparent alfred Y"  However, we want fresh variables: varsOf :: Rule -> [VarId] varsOf = nub . concatMap (foldr (:) []) pretty :: Foldable t => t VarId -> Env -> Subst pretty q (subs, _) = map (second  apply subs)  filter (\(v, _) -> elem v q) subs prolog :: [Rule] -> Exp -> [Subst] prolog rules q = pretty q <> f q pristine where f q = foldr1 (\/!) [refresh [] (varsOf r)  zip ((q ~~):repeat f) r | r <- rules] refresh sub vs zs = case vs of [] -> foldr1 (/\!)  map (\(f, x) -> f  apply sub x) zs (v:vt) -> fresh \w -> refresh ((v, w):sub) vt zs  We called the function prolog because it mimics the logical part of Prolog. We’ve also taken this opportunity to clean up the output. We only print substitutions for variables in the query, and we fully apply all substitutions. We test a classic example: anc :: [Rule] anc = mustRule "ancestor A B :- parent A B" : mustRule "ancestor A B :- parent A X, ancestor X B" : grand exampleAnc = prolog anc  mustFact "ancestor X eadwig"  [[("X",C "edmund")],[("X",C "alfred")],[("X",C "edward")]] ## Out of our depth The following bitty predicate matches all bitstrings of all lengths: bitty = mustRule <> [ "bitty end" , "bitty (0 X) :- bitty X" , "bitty (1 X) :- bitty X" ]  But when we look for them, we only see 0s: exampleBitty = take 5  prolog bitty  mustFact "bitty X"  [[("X",C "end")], [("X",C "0" :@ C "end")], [("X",C "0" :@ (C "0" :@ C "end"))], [("X",C "0" :@ (C "0" :@ (C "0" :@ C "end")))], [("X",C "0" :@ (C "0" :@ (C "0" :@ (C "0" :@ C "end"))))]] Here, depth-first search never tires of the first branch it explores, because it contains infinite matches. Surprisingly, by simply replacing concat with a function that interleaves two lists, the depth-first search gains elements of a breadth-first search in the way it it backtracks. interleave :: [a] -> [a] -> [a] interleave [] ys = ys interleave (x:xs) ys = x : interleave ys xs (\/) :: Pred -> Pred -> Pred (\/) x y e = x e interleave y e (/$$ :: Pred -> Pred -> Pred
(/\) x y e = foldr interleave [] $map y (x e) prolog' :: [Rule] -> Exp -> [Subst] prolog' rules q = pretty q <$> f q pristine where
f q = foldr1 (\/) $(\r -> refresh [] (varsOf r)$ zip ((q ~~):repeat f) r) <$> rules refresh sub vs zs = case vs of [] -> foldr1 (/\)$ map (\(f, x) -> f $apply sub x) zs (v:vt) -> fresh \w -> refresh ((v, w):sub) vt zs  Our search now reaches any given bitstring: exampleBitty' = take 5$ prolog' bitty $mustFact "bitty X"  [[("X",C "end")], [("X",C "1" :@ C "end")], [("X",C "0" :@ C "end")], [("X",C "1" :@ (C "1" :@ C "end"))], [("X",C "0" :@ (C "1" :@ C "end"))]] A cute trick is to define interleave as: interleave xs ys = concat$ transpose [xs, ys]

More generally, the function transpose can interleave multiple lists, which suggests the following variation with even more emphasis on breadth over depth:

disj :: [Pred] -> Pred
disj xs e = concat . transpose $map ($ e) xs

conj :: [Pred] -> Pred
conj = foldr1 \x y e -> concat . transpose $map y (x e) prolog'' :: [Rule] -> Exp -> [Subst] prolog'' rules q = pretty q <$> f q pristine where
f q = disj $(\r -> refresh [] (varsOf r)$ zip ((q ~~):repeat f) r) <$> rules refresh sub vs zs = case vs of [] -> conj$ map (\(f, x) -> f $apply sub x) zs (v:vt) -> fresh \w -> refresh ((v, w):sub) vt zs  ## Predicate combinators Just as we mix and match parser combinators to build parsers, we can mix and match the above functions to build logic programs. For example, the following finds all pairs of lists that concatenate to [3, 1]: appendo :: Exp -> Exp -> Exp -> Pred appendo x y z = ((x ~~ C "nil") /\ (y ~~ z)) \/ fresh \h -> fresh \xt -> fresh \zt -> (x ~~ (C "cons" :@ h :@ xt)) /\ (z ~~ (C "cons" :@ h :@ zt)) /\ appendo xt y zt exampleAppendo = pretty ["X", "Y"] <$>
appendo (V "X") (V "Y") (mustFact "cons 3 (cons 1 nil)") pristine

[[("Y",(C "cons" :@ C "3") :@ ((C "cons" :@ C "1") :@ C "nil")),("X",C "nil")],
[("Y",(C "cons" :@ C "1") :@ C "nil"),("X",(C "cons" :@ C "3") :@ C "nil")],
[("Y",C "nil"),("X",(C "cons" :@ C "3") :@ ((C "cons" :@ C "1") :@ C "nil"))]]

## Prolog forever

To perform true breadth-first search, we define a variant of Pred that takes an Env to a stream of lists of possible Env values that satisfy the predicate. All solutions in the same list have the same cost. The nth list holds all solutions of cost n.

type PredB = Env -> [[Env]]


The unification predicate costs nothing:

(~~~) :: Exp -> Exp -> PredB
s ~~~ t = \(sub, vs) -> [[(s', vs) | s' <- mgu s t sub]]


Disjunction concatenates all solutions with the same cost:

mct = map concat . transpose

disB :: [PredB] -> PredB
disB xs e = mct $($ e) <$> xs  To form the conjunction of f and g, we order the solutions of f by cost, then solve each one of them for g, adding in the cost of solving f before ordering them. We add a cost of 1 to everything by prepending an empty list of solutions, which indicates there are no solutions of cost 0. Conceptually, we want: \f g e -> [[], mct$ zipWith (++) (inits $repeat [])$ mct . map g <$> f e] This only works for finite cases, because we may wind up concatenating an infinite list of []. We know this does nothing, but the code doesn’t! We instead explicitly limit concatenation, resulting in something like a discrete convolution applied to many lists: andB :: PredB -> PredB -> PredB andB f g e = [] : map concat (conv []$ mct . map g <$> f e) where conv acc xs = case xs of [] -> transpose acc (xsh:xst) -> hs : conv (xsh : ts) xst where (hs, ts) = unzip [(h, t) | h:t <- acc] conB :: [PredB] -> PredB conB = foldr1 andB  Generating a fresh variable remains the same, and with that, we have a Prolog that performs breadth-first search: freshB :: (Exp -> PredB) -> PredB freshB f = \(sub, v:vs) -> f (V$ '_':show v) (sub, vs)

prologB :: [Rule] -> Exp -> [Subst]
prologB rules q = pretty q <$> concat (f q pristine) where f q = disB$ (\r -> refresh [] (varsOf r) $zip ((q ~~~):repeat f) r) <$> rules
refresh sub vs zs = case vs of
[]     -> conB $map (\(f, x) -> f$ apply sub x) zs
(v:vt) -> freshB \w -> refresh ((v, w):sub) vt zs


Poorly written recursive rules in Prolog can get trapped in depth-first search. Breadth-first search is more tolerant:

wrong :: [Rule]
wrong =
mustRule "ancestor A B :- parent A B"
: mustRule "ancestor A B :- ancestor X B, parent A X"
: grand

exampleWrong = prologB wrong $mustFact "ancestor X eadwig"  The code still runs forever, but at least it finds all solutions: [[("X",C "edmund")],[("X",C "edward")],[("X",C "alfred")] Putting the output of a Pred in a singleton list to gives us a PredB. Thus we can mix in our other functions and combine different search strategies. ## Program synthesis We employ breadth-first search to find combinatory logic programs F satisfying F x y = y. step :: Exp -> Exp -> PredB step lhs rhs = freshB \x -> freshB \y -> freshB \z -> disB [ conB [lhs ~~~ (C "k" :@ x :@ y) , rhs ~~~ x] , conB [lhs ~~~ (C "t" :@ x :@ y) , rhs ~~~ (y :@ x)] , conB [lhs ~~~ (C "s" :@ x :@ y :@ z), rhs ~~~ (x :@ z :@ (y :@ z))] , conB [lhs ~~~ (C "b" :@ x :@ y :@ z), rhs ~~~ (x :@ (y :@ z))] , conB [lhs ~~~ (C "c" :@ x :@ y :@ z), rhs ~~~ (x :@ z :@ y)] , conB [lhs ~~~ (x :@ z), rhs ~~~ (y:@ z), step x y] ] cl :: Exp -> Exp -> PredB cl lhs rhs = disB [ step lhs rhs , freshB \x -> conB [step lhs x, cl x rhs] ] elemConst :: String -> Exp -> Bool elemConst s t = case t of C s' | s == s' -> True a :@ b -> elemConst s a || elemConst s b _ -> False exampleCL = filter (\[(_, t)] -> not (elemConst "y" t))$ pretty ["F"] <\$>
concat (cl (mustFact "F x y") (mustFact "y") pristine)

[[("F",C "s" :@ C "k")],
[("F",C "c" :@ C "k")],
[("F",C "k" :@ ((C "s" :@ C "k") :@ V "_9"))],
[("F",C "k" :@ ((C "c" :@ C "k") :@ V "_12"))],
[("F",(C "b" :@ (C "s" :@ C "k")) :@ V "_5")],
[("F",(C "b" :@ (C "c" :@ C "k")) :@ V "_5")],
[("F",C "s" :@ (C "c" :@ (C "s" :@ C "k")))],
[("F",C "s" :@ (C "c" :@ (C "c" :@ C "k")))],
[("F",C "c" :@ (C "c" :@ (C "s" :@ C "k")))],
[("F",C "c" :@ (C "c" :@ (C "c" :@ C "k")))],
[("F",(C "k" :@ (C "s" :@ C "k")) :@ V "_8")],
...

## The solution to confusion is substitution

Long ago, I was underwhelmed by Prolog. Introductory examples suggested I could declare facts and rules, then watch the computer magically answer my queries. This was true, up to a point. But past this point, the programmer is forced to peek under the hood, where it turns out a mundane depth-first search is running the show.

We can break free of its limitations with "extra-logical" features such as cut. Understanding side effects even lets us perform breadth-first searches. I admire the ingenuity, but why not use a conventional language if I must muck with low-level details anyway?

I’m happy that modern logic programming seems more principled. Answer set programming (ASP) really does magically answer queries after logical facts and rules are declared. Instead of depth-first search, it uses state-of-the-art SAT-solver techniques.

Researchers are also tidying up by investigating, for example, how types can help, or how to apply equational reasoning.

Seres' thesis on the algebra of logic programming features multiple search strategies using what we called "predicate combinators". The miniKanren language family seems similar. We took our appendo example from a miniKanren demo.

Edward Kmett’s talk on Guanxi contains good references and applications. My initial impression is he is combining all of the above: speedy SAT-solving techniques coupled with a set of building blocks for rich data structures such as abstract syntax trees.

My starting point was the paper on microKanren, which uses a Lisp-like language. I found simplifications thanks to Haskell’s lazy evaluation, and list monad (only to discover later these improvements were well-known). On the other hand, Lisp’s homoiconicity is certainly seductive.

The literature mentions monads frequently (there’s even a LogicT monad transformer) but I shied away; I’m apprehensive about committing to particular choices for (<|>) and (>>=).

Ben Lynn blynn@cs.stanford.edu 💡