The Concurrent Constraint Programming Research Programmes -- Redux, Invited Talk CP2014 in Lyon Vijay Saraswat

1. IBM Research: Computing as a Service
Combinatorial Problem Solving in C10
How to write programmable solvers declaratively
Vijay Saraswat
<firstname>@<lastname>.org
IBM TJ Watson
Sep 9, 2014
© 2005 IBM Corporation Computing as a Service 1

2. CCP Research Programmes
Logic Programming
 In the early 80s, Colmerauer /
Kowalski and colleagues developed
definite clause logic programming
based on a procedural interpretation
of proofs (“right hand side”
computation, backward chaining)
– Operationally one gets non-deterministic
user-defined recursive
procedures, accumulating constraints
over Herbrand terms.
– Logically, given an atom p(X1, …, Xn),
the system generates bindings X1=t1,
…, Xn=tn sufficient to establish the truth
of the atom, given the program
clauses.
– Multiple (implicitly disjunctive) answers
may be returned.
conjunction disjunction
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(Goal) G ::= H|G,G|G;G
(Program) P ::= H:-G|P,P
(Atom) H ::= p(t1,…,tn)
X1=t1, …, Xn=tn ?- p(X1,…,Xn)
Answer returned by Query posed by user
system

3. CCP Research Programmes
Prolog 3 and Constraint Logic Programming
 At POPL 87, Jaffar and Lassez
showed how this framework could
be extended to (essentially) arbitrary
constraints, implemented by an
embedded (black box) constraint
solver
– Atomic formulas drawn from some
underlying constraint theory (over a
vocabulary disjoint from the user-defined
predicates E)
 CLP(R) is an exemplar, permitting
linear arithmetic constraints over R.
 Prolog III can also be viewed as an
instance, over a different, rich
constraint system
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G ::= c
c1, …, ck ?- p(X1,…,Xn)

4. CCP Research Programmes
However, …
 This framework is not flexible
enough to permit the user to specify
propagation rules, or search
strategies in logic.
 Often constraint solvers are
incomplete, or highly combinatorial
and user-defined propagation rules
are critical (cf cc(FD))
 Similarly often critical are search
strategies, e.g. run all propagation
rules, then expand (non-deterministically)
Research
the atom with the
fewest choices left (cf CHIP)
IBM © 2009 IBM Corporation 4
X in {1,2,3}, X!=Y, Y=2
?- X in {1,3}

5. CCP Research Programmes
Claim: (Timed) CCP provides that framework
 Concurrent Constraint Programming is a
logical framework based, dually, on the
notion of Agents, not Goals, on “left hand
side” computation (“forward chaining”)
 Operationally one gets non-deterministic
user-defined recursive procedures,
accumulating constraints in a shared
store.
 Implicative agents (if c D) trigger on the
presence of a constraint c in the store,
and take further actions.
(Agent) D ::= E|c|D,D
|D;D|if c D
(Program) P ::= E-:D|P,P
(Atom) E ::= p(t1,…,tn)
p(X1,…,Xn) ?- c1 ; … ; ck
Entailed constraints
determined by
system
Agent proposed by
user
Research
 Disjunction is “angelic” – on the LHS, if B
entails A, then (A;B) is identical to A, i.e
IBM the more general solution (A) is kept.
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X in {1,2,3}, X!=Y, Y=2
?- X in {1,3}

6. CCP Research Programmes
Example: Map Coloring
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is specializable to individual backend solvers, so they can control what form constraints end
up in. In particular, MiniZinc allows the specification of global constraints by decomposition.
2 Basic Modelling in MiniZinc
In this section we introduce the basic structure of a MiniZinc model using two simple exam-ples.
2.1 Our First Example
Figure 1: Australian states.
As our first example, imagine that we wish to colour a map of Australia as shown in
Figure 1. It is made up of seven different states and territories each of which must be given
a colour so that adjacent regions have different colours.
We can model this problem very easily in MiniZinc. The model is shown in Figure 2. The
first line in the model is a comment. A comment starts with a ‘%’ which indicates that the
rest of the line is a comment. MiniZinc has no begin/ end comment symbols (such as C’s / *
and * / comments).
The next part of the model declares the variables in the model. The line
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class OzMap(N:Int) {
type Colors=1..N.
enum States={wa,nt,sa,nsw,v,t,q}.
X:Map[States,Colors].
agent map {
X(wa)!= X(nt), X(wa)!=X(sa),
X(nt)!= X(sa), X(nt)!=X(q),
X(sa)!= X(q), X(sa)!=X(nsw),
X(sa)!=X(v),
X(q) != X(nsw),X(nsw)!=X(v),
all (i in X.domain)
choose(X(i), 0, X(i).values)
}
agent choose[T](X:T, i:Int, v:Rail[T]){
if (i < V.size)
Note: type-generic choose
X=v(i);
choose(X, i+1, v)
}
}
O=new OzMap(3), O.map |-
X(wa)=1,X(nt)=2, X(sa)=3,
X(qa)=1, X(nsw)=2, X(v)=1,
X(t)=1
?
But no control between propagation and choice!

7. CCP Research Programmes
Another example: Zebra
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class Zebra {
enum Nationalities = {english, spanish, ukrainian, norwegian, japanese}.
enum Colors = {red, green, ivory, yellow, blue}.
enum Animals = {dog, fox, horse, zebra, snails}.
enum Drinks = {coffee, tea, milk, orangeJuice, water}.
enum Cigarettes = {oldGold, kools, chesterfields, luckyStrike, parliaments}.
type Houses = Int(0,4).
vars: Array(0..4,0..4)[Houses].
Nation = variables(0), Color = variables(1), Animal = variables(2),
Drink = variables(3), Smoke = variables(4).
agent rightof(h1:Houses, h2:Houses){ h1=h2+1}
agent nextto(h1:Houses, h2:Houses) { rightof(h1,h2) ; rightof(h2,h2)}
agent middle(h:Houses) {h=3}
agent left(h1:Houses) {h=1}
agent constraints {
alldifferent(Nation), alldifferent(Color), alldifferent(Animal),
alldifferent(Drink), alldifferent(Smoke),
Nation(english) = Color(red), Nation(spaniard) = Animal(dog),
Drink(coffee) = Color(green), Nation(ukrainian) = Drink(tea),
rightof(Color(green), Color(ivory)),
…}

8. CCP Research Programmes
Zebra – but the “control” is programmable
agent alldifferent[T](A:Array[T]) {
all (i in A.domain, j in A.domain{j !=i})
A(j) != A(i) // assuming != available as a constraint on two variables
}
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// Alternate
agent alldifferent[T](A:Array[T]) {
all (i in A.domain, j in A.domain{j !=i})
all (k in A(i).domain)
if (A(i) = k) A(j) != k // removes k from A(j)’s domain
}
alldifferent/1 – a “global constraint” – is just a user-defined propagator.
If A(i)=k (for any value k and index i) then k is removed from the domain of all
other variables A(j).

9. CCP Research Programmes
But how do we ensure propagation before choice?
 Use time – Timed CCP.
 TCC is obtained by extending CC
“uniformly” across time. A CC
computation is run at time t (starting with
t=0) till quiescence. Then all agents A
s.t. the current time instant has the agent
next A are collected, and executed at
the next time instant.
– Thus TCC provides a logical way to
arrange executions in a total order.
 In TCC, the store may be changed non-monotonically
between time instants.
Research
 This is not possible in Punctuated CCP
– here all constraint tells are done within
an always, hence constraints persist
IBM across time.
© 2009 IBM Corporation 9
(Agent) D ::= next c D
(Program) P ::= E-:D|P,P
(Atom) E ::= p(t1,…,tn)
p(X1,…,Xn) ?-
(c1
1
,next(c1
2,next(…c1
m
1)…));
…;
(ck
1
,next(ck
2, next(…ck
m
k)…))
Entailed constraints,
across time
determined by the
system
Agent proposed by
user
The result is (c1
m
1; …; ck
m
k).

10. CCP Research Programmes
Zebra – but the “control” is programmable
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agent solve {
(I,J) = argmin((i,j:vars.domain)=>values(vars(i,j)).size),
if (values(vars(I,J)).size > 1) next {
always choose(vars(I,J)),
next solve()
}
}
values is the only “primitive” indexical – returns a rail of values for its argument
variable.
solve implements the strategy of alternating between time instants in which propagation
happens, and time instants in which the decision is made of the variable to split. It
terminates when no more variables are left to split.
(I,J) is the index s.t. values(var(I,J)).size is minimized. If there are k > 1
values, choose (i.e. branch disjunctively k-ways) in the next time instant.
This will automatically cause propagation (in that time instant).

11. CCP Research Programmes
Zebra – but the “control” is programmable
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public def agent main(String[Rail]) {
z = new Zebra,
always z.constraints,
always all (i,j in z.vars.domain)
if (vars(i.j).size <=1) choose(vars(I,J))
next z.solve
}
The main method. Creates a new Zebra problem, asserts its constraints in all
time instants, and sets up a propagator that forces the value of any variable
as soon as it has only one value left in its extent.
Also sets up the solve agent to alternate between propagation and choice

12. CCP Research Programmes
(Aside: In fact RCC gives you CCP+CLP and more)
 Much richer capabilities in RCC, while
staying within the paradigm
– Fully recursive goals (CLP) are available.
– Agents with deep guards permit triggers to
be recursively defined
– Goals with deep guards permit conditional
recursive augmentation of the store, for the
purposes of answering the goal
– Universal goals permit parametric goal
solving
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(Agent) D ::= E|c|D,D
|D;D|if G D
|all X D
|some X D
(Goal) G ::= H|c|G,G
|G;G|if D G
|all X D
|some X D
(Program) P ::= E-:D|P,P
(Atom) E ::= p(t1,…,tn)
p(X1,…,Xn) ?- c1 ; … ; ck

13. CCP Research Programmes
Crossgrams
 A puzzle suggested to me by Gopal Raghavan.
 Find words w in the English language which are such that for each
position I in w there is a distinct anagram of w starting with the letter
at the i’th position.
– Example: emits – the corresponding anagrams are mites, items,
times, smite.
 Here we consider the simpler version that drops the distinctness
requirement (just to keep the program slightly smaller).
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14. CCP Research Programmes
Key to solution
 Given a list of N dictionary words W, generate N facts
word(W, L, S, Ws) where
– L is the first character of W,
– Ws is a (possibly non-English word) anagram of W in which
all letters are in increasing sort order, and
– S is the first letter of Ws.
– E.g. word(emits, e, e, eimst) / word(items, i,
e, eimst) / word(mites, m, e, eimst), …
 With these clauses, crossgrams can be generated
quickly by backtracking (assuming facts are indexed
appropriately, as they are in many Prolog systems).
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15. CCP Research Programmes
Crossgrams: illustrates use of if D G goals
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class Crossgram {
@Gentzen def word(Atom, Char, Char, Atom).
goal crossgrams(Dict: List[Atom]) = R!{
if (all (W in Dict) {
W = name(Wls),
Wsls = Wls.msort(),
word(W, Wls.head, Wsls.head, name(Wsls))
})
words(R)
}
goal words(R:List[Atom]) {
word(A,C,C,Ws), A=name(Wls),
R=List(A, Wls.tail.map((L:Char)=>W!{word(W,L,_,Ws)}))
}}
Assert word/4
constraints on the fly,
based on the dictionary,
add them to the store.
Solve goal in context of generated constraints
This could fail, triggering
backtracking.

16. CCP Research Programmes
Conclusion
 (Timed) CCP provides a dual logic programming
framework which is much closer to more conventional
“constraint programming” than CLP.
 In particular, TCC permits the use of user-defined
propagators and search procedures.
 R[T]CC considerably enriches programming power,
beyond TCC.
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