Constraint propagation
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Transcript Constraint propagation
Automated Reasoning
Systems
For first order Predicate Logic
AR: general context.
Given is a knowledge base in predicate logic: T
is a set of formulae in first order logic
formally also called: a “Theory’’
Given is also an additional first order formula: F
Is F a logical consequence of T ?
Notation: T |= F
(T implies F)
Find reasoning techniques that allow to decide on
this for EACH F and T.
Requirements:
correctness -- completeness
-- efficiency
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AR: decidability.
Theorem Church ‘36:
There CANNOT EXIST AN ALGORITHM that decides
whether T |= F, for any theory T and any formula F.
BUT: semi-decidable !
Completeness Theorem of Goedel ‘31:
There exists a reasoning technique, such that for any
theory T and formula F, such that T |= F, the
reasoning technique proves T |= F.
SO: if F follows from T, then we find a proof, else it is
possible that the procedure doesn’t terminate.
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Wait a second ...
The theorems of Church and Goedel are contradicting:
We can try to prove F and ~F in parallel, and according
to Goedel’s theorem one of these must succeed after a
finite time.
Wrong !
Let: T = {smart(Kelly)} en F = strong(Kelly)
Although strong(Kelly) ~strong(Kelly) is always true,
we have:
neither: {smart(Kelly)} |= strong(Kelly)
nor: {smart(Kelly)} |= ~strong(Kelly)
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AR: general outline (1).
First we sketch the most generally used approach
for automated reasoning in first-order logic:
backward resolution
The different technical components will only be
explained in full detail in a second pass (outline (2)).
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AR: general outline (2) .
We study different subsets of predicate logic:
ground Horn clause logic
Horn clause logic
Clausal logic
full predicate logic
In each case we study semi-deciding procedures.
Each extension requires the introduction of new
techniques.
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Backward Reasoning
Resolution
… in a nutshell
Backward resolution:
0) The task: an example.
1) Proof by inconsistency.
2) Conversion to clausal form.
3) Unification.
4) The resolution step.
5) (Backward) resolution proofs.
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0) The TASK (example):
T
z ~ q(z)
y p(f(y))
x p(x) q(x) r(x)
p = parent
f = father
r = rich
q = old
Are axioms: describe knowledge about some world.
In this world, is
F
u r(f(u))
always true ?
How to prove such theorems in general?
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1) Proof by inconsistency
Don’t prove F directly:
F
u r(f(u))
But add the negation of F to the axioms and prove
that this extension is inconsistent.
NEW TASK:
is inconsistent.
~ u r(f(u))
z ~ q(z)
y p(f(y))
x p(x) q(x) r(x)
the 4 axioms are never true in 1 same interpretation.
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2) Clausal form:
= Normalize the formulae to a (more simple) standard form.
Each set of axioms can be transformed into a new set of
formulae, that contains only formulae of the form:
xy…z p(…) q(…) … r(…) t(…) s(…) … u(…)
only left; only right
no ~ ; no
which is inconsistent if and only if the original set was
inconsistent.
Notice: “x y … z“ can be dropped.
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Example:
~ u r(f(u))
u false r(f(u))
z ~ q(z)
z false q(z)
y p(f(y)) : is already in clausal form: ( P P true)
x p(x) q(x) r(x)
x q(x) r(x) p(x)
Ps: usually requires much more work!
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3) Unification:
Given 2 atomic formulae:
Ex.:
p(f(A),y)
p(x, g(x))
find their most general common instance.
Ex.:
x must become: f(A)
g(x) must become: g(f(A))
y must become: g(f(A))
p(f(A), g(f(A)))
Most general unifier (mgu) :
x -> f(A)
y -> g(f(A))
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4) The resolution step
Proposition logic:
P Q
P
Q
QP
P true
Q true
P Q
~Q
~P
QP
false Q
false P
P1 P2 … Pn Q1 ... Qm
R1 … Rk P1 S1 … Sl
P2 … Pn R1 … Rk Q1 ... Qm S1 … Sl
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De resolutie step (2):
Predicate logic:
Example:
mgu(p(x,f(A)), p(B,z)) =
x -> B
z -> f(A)
p(x,f(A)) q(g(x))
r(z) p(B,z)
r(f(A)) q(g(B))
= mgu applied to r(z) q(g(x))
Clauses on which resolution is performed
must not have any variables in common.
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5) Resolution proofs:
In order to prove a set of clauses inconsistent:
select 2 of them, for which resolution is possible
apply resolution and add the result to the set
if you obtain the clause false : STOP !
This means inconsistency of the last set
AND inconsistency of the original set
AND that F was implied by T
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Example:
q(x) r(x) p(x)
false r(f(u))
x -> f(u)
q(f(u)) p(f(u))
false q(z)
z -> f(u)
false p(f(u))
p(f(y))
y -> u
So: inconsistent !
false
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A deeper study:
Ground Horn Logic
Horn Logic
Clausal Logic
Full Predicate Logic
Modus ponens
Unification
Resolution
Normalization
Horn clause logic
All formulae in T are of the form:
x1 … xk
A B1 B2 … Bn
where A, B1, B2,…,Bn are atoms.
An atom is a formula of the form p(t1,…,tm), with p a
predicate symbol and t1,…,tm terms.
Horn clause formulae are universally quantified over
all variables that occur in them.
B1,…,Bn are called body-atoms of the Horn clause; A
is the head of the Horn clause.
n may be 0: in this case we say that the Horn clause
is a fact.
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Alternative notation:
Alternatively one can write a Horn Clause:
x1 … xk
as:
x1 … xk
A B1 B2 … Bn
Q P
Q ~P
A ~B1 ~B2 … ~Bn
This is a special case of the “conjunctive normal
form” (only disjunctions and negations), with only 1
positive disjunct.
Implicative form
Conjunctive form
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Wich kind of formulae
can we prove?
In Horn clause logic, we limit ourselves to prove
formulae F of the form:
x1 … xk B1 B2 … Bn
where B1, B2, …, Bn are again atoms.
All variables are existentially quantified !
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A very simple example:
Bosmans is a showmaster
(1)
Showmasters are rich
(2)
Rich people have big houses
(3)
Big houses need a lot of maintenance
(4)
Goal: automatically deduce that Bosmans’ house
needs a lot of maintenance.
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Representatie in Horn logica:
Bosmans is a showmaster
showmaster(Bosmans)
Showmasters are rich
p rich(p) showmaster(p)
Rich people have big houses
(1)
(2)
(3)
p big(house(p)) rich(p)
Big houses need a lot of maintenance
(4)
p lot_maint(house(p)) big(house(p))
To prove:
Lot_maint(house(Bosmans))
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AR for ground
Horn clause logic
Backward (and forward) reasoning
proof procedures based on generalized
Modus Ponens
Restricting to ground
Horn clauses:
So, for now: Horn clauses without variables:
Example:
showmaster(Bosmans)
rich(Bosmans) showmaster(Bosmans)
big(house(Bosmans)) rich(Bosmans)
lot_maint(house(Bosmans)) big(house(Bosmans))
Prove:
lot_maint(house(Bosman))
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Easy with modus ponens !
3 applications of modus ponens:
showm(Bos)
rich(Bos) showm(Bos)
rich(Bos)
big(house(Bos)) rich(Bos)
big(house(Bos))
lot_maint(house(Bos)) big(house(Bos))
lot_maint(house(Bos))
gives the desired conclusion.
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Modus ponens in AR:
Modus ponens is correct:
B
AB
A
For any interpretation making both B
and A B true (= any model of {B , A
B} )
A is also true in this interpretation
(see truth tables)
Problem: how to organize this into a procedure which
is also complete (for ground Horn clauses)?
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Generalized
Modus ponens
Increase the number of conjuncts:
B1
B2
…
Bn
A B1 B2 … Bn
A
Is obviously still correct (truth tables).
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A glimpse at a
Forward Reasoning
Modus Ponens strategy
A forward proof procedure
Given theory T and formula F:
Derived := { };
Repeat
Select some A B1 B2 … Bn from T,
such that all B1, B2, … , Bn Derived,
and A Derived;
Derived:= Derived {A};
Until all atoms of F = C1 C2 … Cm are in Derived
or no more Selection are possible
If all atoms from F are in Derived at termination,
then T implies F, otherwise it doesn’t.
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Extended example:
showm(Bos) european(Bos) belg(Bos)
belg(Bos)
rich(Bos) showm(Bos) european(Bos)
big(house(Bos)) rich(Bos)
lot_maint(house(Bos)) big(house(Bos))
One possible derivation:
Step 0:
Step 1:
Step 2:
Step 3:
Step 4:
Step 5:
End :
Derived := { }
Derived := {showm(Bos)}
Derived := Derived {belg(Bos)}
Derived := Derived {european(Bos)}
Derived := Derived {rich(Bos)}
Derived := Derived {big(house(Bos))}
Derived := Derived {lot_maint(house(Bos))}
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Remarks:
Correctness: generalized modus ponens is correct
Completeness: intuition:
for a finite ground Horn clause theory, only a finite
number of ground atoms are implied
these are all derived after finite time
Efficiency:
can be extremely slow !
If T contains many Horn clauses unrelated to F, then
the procedure derives many irrelevant (for F ) atoms.
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Backward reasoning
For ground Horn Clause logic
Inconsistency:
A theory T is inconsistent if it has NO model.
Theorem:
Let T be a theory and F a formula.
T implies F if and only if T {~F} is inconsistent.
Proof:
T implies F
iff
iff
iff
iff
Each model of T makes F true
Each model of T makes ~F false
T {~F} has no model
T {~F} is inconsistent
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The example again:
Prove that the theory:
showm(Bos)
belg(Bos)
european(Bos) belg(Bos)
rich(Bos) showm(Bos) european(Bos)
big(house(Bos)) rich(Bos)
lot_maint(house(Bos)) big(house(Bos))
~ lot_maint(house(Bos))
is inconsistent.
Problem: this is NOT a Horn clause theory !?
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Refutation proofs:
the “false” predicate
We introduce a new predicate symbol:
false
We agree that false has the truth value ‘false’
under every interpretation.
Imagine that we defined false as :
false p ~p
for some predicate p
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“definite” goals:
In the Horn logic setting F has the form:
x1 … xm B1 B2 … Bn
So what is the form of ~F?
~(x1 … xm B1 B2 … Bn)
x1 … xm ~(B1 B2 … Bn)
x1 … xm false ~(B1 B2 … Bn)
x1 … xm false B1 B2 … Bn
A ~B A B
Observe: ~F is again a Horn clause !!
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In conjunctive normal form:
Again:
x1 … xm false B1 B2 … Bn
is equivalent with:
x1 … xm false
~B1 ~B2 … ~Bn
x1 … xm ~B1 ~B2 … ~Bn
0 positive disjuncts !
As a special case, “false” is the empty disjunction:
false
Implicative en disjunctive form remain consistent
(an empty disjunction is always false)
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Back to the example
The extended theory (to be proven inconsistent)
now is:
showm(Bos)
belg(Bos)
european(Bos) belg(Bos)
rich(Bos) showm(Bos) european(Bos)
big(house(Bos)) rich(Bos)
lot_maint(house(Bos)) big(house(Bos))
false lot_maint(house(Bos))
a ground Horn clause theory !
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Modus ponens
generalized some more:
A B1 B2 … Bi … Bn
Bi C1 C2 … Cm
A B1 B2 … C1 C2 … Cm … Bn
Ordinary Modus ponens is the special case with:
n = i = 1 and m =0
Correctness: via truth tables
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Some backward reasoning
steps in the example:
false lot_maint(house(Bos))
lot_maint(house(Bos)) big(house(Bos))
false big(house(Bos))
false big(house(Bos))
big(house(Bos)) rich(Bos)
false rich(Bos)
and so on ...
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The backward procedure:
the idea
Convert F into a definite goal:
false B1 B2 … Bi … Bn
Apply generalized modus ponens to the body-atoms Bi
of the goal, using the Horn clauses of T
until:
false
is deduced.
Then: a false formula ia a consequence of T {~F}
we have proven inconsistency of T {~F}
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Backwards procedure
Goal := false B1 B2 … Bn ;
Repeat
Select some Bi atom from the body of Goal
Select some clause Bi C1 C2 … Cm from T
Replace Bi in the body of Goal by C1 C2 … Cm
Until Goal = false or no more Selections possible
On top of this you need to apply backtracking over
the selected clauses and the selected body atoms.
If the algorithm stops because it has tried all these
alternatives: F was not implied!
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Back to the example
Step 0: Goal := false lot_maint(house(Bos))
select: lot_maint(house(Bos)) big(house(Bos))
Step 1: Goal := false big(house(Bos))
select: big(house(Bos)) rich(Bos)
Step 2: Goal := false rich(Bos)
select: rich(Bos) showm(Bos) european(Bos)
Step 3: Goal := false showm(Bos) european(Bos)
select: showm(Bos)
Step 4: Goal := false european(Bos)
select: european(Bos) belg(Bos)
Step 5: Goal := false belg(Bos)
select: belg(Bos)
Step 6: Goal := false
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Another example
(propositional)
pqr
qt
qs
rn
ro
s
o
n
Prove: p
Observe: non-determinism on both atom selection
and on clause selection !
we only illustrate the clause selection here
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Search tree traversed by
the backward procedure
false p
false q r
false t r
false s r
false r
pqr
qt
qs
rn
ro
s
o
n
false n
false o
false
false
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Backward procedure
is more efficient
The proof is now goal directed towards the theorem.
no more exploration of irrelevant rules
Different search methodes can be used to traverse
this search tree.
Atom-selection may influence efficiency too:
ex.: by detecting a failing branch sooner
but has no impact on whether or not we find a solution
(in case there are only finitely many ground Horn clauses)
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Completeness:
Example:
false p
pp
p
(1)
(2)
Possible derivations:
(2)
false
false p
(1)
false p
(1)
false p
Is only complete if the search tree is
traversed using a complete search method.
(1)
……
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Representation-power of
ground Horn clauses
Is a subset of propositional logic.
Example:
showm(Bos)
showm_Bos
big(house(Bos))
big_house_Bos
In general, more expressive logics are needed.
Essence: with variables, one formula may be equivalent
to a very large number of propositional formulae.
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