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Horn Complements: Towards Horn-to-Horn Belief Revision
Marina Langlois and Robert H. Sloan
University of Illinois at Chicago
mirodo1 | sloan@uic.edu
Bal´ azs Sz¨ or´ enyi
Hungarian Academy of Sciences & U. of Szeged
szorenyi@inf.u-szeged.hu
Gy¨ orgy Tur´ an
University of Illinois at Chicago,
Hungarian Academy of Sciences & U. of Szeged
gyt@uic.edu
Abstract
Horn-to-Horn belief revision asks for the revision of a Horn
knowledge base such that the revised knowledge base is also
Horn. Horn knowledge bases are important whenever one
is concerned with efficiency—of computing inferences, of
knowledge acquisition, etc.
could be of interest, in particular, as a component of any effi-
cient system requiring large commonsense knowledge bases
that may need revisions because, for example, new contradic-
tory information is acquired.
Recent results on belief revision for general logics show that
the existence of a belief contraction operator satisfying the
generalized AGM postulates is equivalent to the existence of
a complement. Here we provide a first step towards efficient
Horn-to-Horn belief revision, by characterizing the existence
of a complement of a Horn consequence of a Horn knowledge
base. A complement exists if and only if the Horn conse-
quence is not the consequence of a modified knowledge base
obtained from the original by an operation called body build-
ing. This characterization leads to the efficient construction
of a complement whenever it exists.
Horn-to-Horn belief revision
Introduction
Revising a knowledge base in the presence of new, po-
tentially conflicting information is a basic task facing a
commonsense reasoning agent. Belief revision usually ap-
proaches this task by identifying postulates that should be
satisfied by a rational revision operator, such as the AGM
postulates (Alchourr´ on, G¨ ardenfors, and Makinson 1985),
and characterizing operators that satisfy these postulates
(G¨ ardenfors 1988; Hansson 1999). The basic operators are
belief revision, when a new, perhaps contradictory belief is
to be incorporated into the knowledge base, and belief con-
traction, when an undesirable consequence is to be removed
from the knowledge base. One often considers contraction
first, and then defines revision in a standard way in terms of
contraction. Most of the work in belief revision assumes that
the underlyinglogic includes full propositional logic. On the
other hand, motivated in part by efficiency considerations, in
many applications one uses a logic based on only a fragment
of propositional logic.
Copyright c ? 2008, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
Adapting belief revision to this more general situation has
been initiated by the recent work of (Flouris, Plexousakis,
and Antoniou 2004). They study belief revision in general
logics, and formulate a property called decomposability of
thelogic. Itisshownthatdecomposabilityisanecessaryand
sufficient condition for the existence of an AGM-compliant
belief contraction operator. This framework is then used in
(Flouris, Plexousakis, and Antoniou 2005) to study decom-
position properties of description logics, motivated by appli-
cations to the Semantic Web.
In order to build a powerful agent capable of common-
sense reasoning, one fundamental challenge is to integrate
various capabilities, including belief revision, that have been
studied so far mostly in isolation. A commonsense reason-
ing agent should execute its tasks efficiently, and therefore it
has to use some tractable knowledge representation. In this
paper we explore an approach that takes tractable knowl-
edge representation as the primary constraint, and consid-
ers other requirements, in particular rationality constraints,
as also important, but secondary. For belief revision, this is
a departure from the standard framework.
Horn formulas provide a natural candidate for a general
framework for the integration process. This fragment of
propositional logic is expressive, allows for polynomial time
inference, and indeed is generally computationally tractable,
which explains its central role in artificial intelligence and
computer science. In particular, belief revision with Horn
formulas has been studied extensively (see references in the
next section). However, the problem of belief revision that
maintains a Horn knowledge base throughout the revision
process apparently has not been studied. Here we consider
such Horn-to-Horn revisions. That is, we are interested in
the possibilities and limitations of revising Horn formulas
such that the revised formula is also Horn. This, then, is
a special case of the general framework of (Flouris, Plex-
ousakis, and Antoniou 2004), and thus their general char-
acterization for the existence of an AGM-compliant belief
contraction operator applies. Horn logic as a whole turns
out not to be decomposable. Thus one must ask the more
detailed question of when contraction is possible. More pre-
cisely, one is led to the following problem, which may also
be of interest in itself.
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Problem A. For Horn formulas ϕ and ψ, where ψ is a con-
sequence of ϕ, when does there exist a proper Horn conse-
quence χ of ϕ, such that ψ ∧ χ is equivalent to ϕ?
Such a formula χ is called a ϕ-complement of ψ, and it
corresponds to the result of a contraction operator, when the
formula ψ is contracted from the knowledge base ϕ. For the
decomposability framework, it would be desirable always to
have a complement, but unfortunately this is not the case.
(See Example 9.) When a complement does not exist, one
may try to find an approximate complement.
Our main result (Theorem 7) gives a complete answer to
Problem A by giving two characterizations of all those pairs
ϕ and ψ for which ψ has a ϕ-complement. The character-
izations give efficiently decidable criteria and lead to effi-
cient algorithms to construct a complement, if it exists. The
complements constructed are only polynomially larger than
the original knowledge base (although repeated application
could cause an exponential blowup). As a corollary, one
obtains a complete description of decomposable Horn for-
mulas as well, where a Horn formula is decomposable if all
its Horn consequences have a complement. We also present
some computational results on the fraction of Horn impli-
cates of a random Horn formula having a complement.
The rest of the paper is structured as follows. The next
section gives a brief overview of previous related work. The
section after that gives a more detailed review of Flouris,
Plexousakis, and Antoniou’s AGM-based framework and
their basic results together with the formal connection be-
tween belief revision theory and the rest of this paper. Then
background is provided on Horn formulas. The next three
sections contain our results, a proof sketch of the main theo-
rem and the sketch of another proof of the first characteriza-
tion. The last two sections present the experimental results
and some remarks on further work.
Related work
Horn formulas have been considered previously in several
papers dealing with the complexity of belief revision, e.g.,
(Eiter and Gottlob 1992; Gogic, Papadimitriou, and Sideri
1998; Jin and Thielscher 2005; Liberatore 1997; 2000;
Nebel 1998). The results obtained in these papers are mostly
complexity-theoretic negative results, and they deal with re-
visionmethodswheretherevisionofaHornknowledgebase
is not necessarily Horn, or they propose revision methods
that may be inefficient. A more detailed comparison with
our work will be given in the full version of this paper.
If ψ is a single Horn clause implicate C, then Problem A
can be reformulated as follows: does ϕ have an irredundant
conjunctive normal form expression containing C? The re-
lated question where C is a prime implicate and the irredun-
dant conjunctive normal form expression is also assumed to
consist of prime implicates only, has been studied by (Ham-
mer and Kogan 1995). They call such a prime implicate
non-redundant, and show that non-redundancy is polynomi-
ally decidable for negative clauses, but it is NP-complete for
definite clauses.
General logics and belief contraction
In this section we give a brief outline of the formal frame-
work for belief revision in general logics, following (Flouris,
Plexousakis, and Antoniou 2004). We also indicate how this
very general and abstract framework can be specialized to
Horn logic.
A logic is specified by a set of expressions L and a con-
sequence operator Cn : P(L) → P(L), where P(L) is the
power set of L. The consequence operator is assumed to
satisfy the properties of inclusion (A ⊆ Cn(A)), iteration
(Cn(A) = Cn(Cn(A))) and monotonicity (A ⊆ B implies
Cn(A) ⊆ Cn(B)) for every A,B ⊆ L.
For Horn logic, L is the set of all Horn clauses over a fixed
finite set of variables, and for a set of Horn clauses ϕ, the set
Cn(ϕ) contains all Horn clauses implied by ϕ. The required
properties are clearly satisfied.
A theory or knowledge base K is a subset of L such that
K = Cn(K). For Horn logic, a theory can be specified
by a set of Horn clauses. The corresponding theory then
consists of all their consequences. A contraction operator,
denoted by −, is of the form − : P(L) × P(L) → P(L),
and maps a knowledge base and a set to be contracted to
a new knowledge base, the result of the contraction. The
(generalized) AGM postulates for contraction are1
closure: K − A = Cn(K − A)
inclusion: K − A ⊆ Cn(K)
vacuity: A ?⊆ Cn(K) implies K − A = Cn(K)
success: A ?⊆ Cn(∅) implies A ?⊆ Cn(K − A)
preservation: Cn(A) = Cn(B) implies K − A = K − B
recovery: K ⊆ Cn((K − A) ∪ A).
A contraction operator is AGM-compliant if it satisfies these
postulates. A logic is AGM-compliant if there exists an
AGM-compliant contraction operator for it.
Given a logic, and subsets K,A ⊆ L, the set of comple-
ments of A with respect to K is
A−(K) =
{B ⊆ L : Cn(B) ⊂ Cn(K) and Cn(A ∪ B) = Cn(K)}
if Cn(∅) ⊂ Cn(A) ⊆ Cn(K), and A−(K) = {B ⊆ L :
Cn(B) = Cn(K)} otherwise.
A set K ⊆ L is decomposable if A−(K) ?= ∅ for every
A ⊆ L. A logic is decomposable if every K is decompos-
able. For Horn logic, these definitions specialize to Defini-
tions 3 and 4.
Theorem 1 ((Flouris, Plexousakis, and Antoniou 2004)). A
logic is AGM-compliant iff it is decomposable.
In particular, Flouris, Plexousakis, and Antoniou show
that if a logic is decomposable, then by selecting any com-
plement one obtains an AGM-compliant contraction opera-
tor. More generally, if K is decomposable, then by selecting
1(Flouris, Plexousakis, and Antoniou 2004) refers to these as
generalized versions of the properties, but for simplicity we omit
the term ‘generalized’.
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any complement with respect to K, one obtains a contrac-
tion operator that handles contractions from K in an AGM-
compliant manner. It also follows from their arguments that
if A−(K) ?= ∅ then selecting any complement of A with re-
spect to K handles the contraction of A from K in an AGM-
compliant manner.
Preliminaries
Let U be the set of propositional variables in our universe.
Two clauses collide if they contain a pair of complementary
literals. AclauseisHornifitcontainsatmostoneunnegated
literal, definite if it contains exactly one unnegated literal,
and negative if it contains only negated literals. A (definite)
Horn formula is a conjunction—or a set, whichever view is
more convenient—of (definite) Horn clauses (an empty con-
junction is always true). A Boolean function is a (definite)
Horn function if it has a (definite) Horn formula.
LowercaseRomanalphabet lettersf,g,hdenoteBoolean
functions; lower case Greek letters ϕ,ψ,χ denote Boolean
formulas. If we use ϕ in a place where a function is ex-
pected, then ϕ stands for the Boolean function represented
by ϕ. Two formulas ϕ1 and ϕ2 are equivalent, denoted
ϕ1∼ ϕ2, if they represent the same Boolean function.
For a Horn clause C, let Body(C) be the set of variables
corresponding to the negated literals in C, or their conjunc-
tion (which will be clear from context). Also, let Head(C)
be the unnegated variable of C if C is a definite clause, and
0 if C is a negative clause. We use → to denote the Boolean
implication operator, so Horn clause C can be written as
Body(C) → Head(C). For example, if C is the Horn clause
¯ x∨ ¯ y∨z, then Body(C) = {x,y}, Head(C) is z, and C can
also be written as x,y → z or (x∧y) → z. If C is the Horn
clause ¯ x∨¯ y then it can also be written as x,y → 0 or simply
x,y →.
A Boolean function g is a consequence of Boolean func-
tion f, denoted f ⇒ g, if every assignment that satisfies f
also satisfies g. A function g is a proper consequence of f,
denoted f
implicate of f if f ⇒ C.
The set of satisfying (resp., falsifying) truth assignments
of f is denoted by T(f) (resp., F(f)). A function f is anti-
monotone if T(f) is downward closed, i.e., f(a) = 1 and
b ≤ a imply f(b) = 1.
We will use a slight generalization of anti-monotone func-
tions.
Definition 2 (Almost anti-monotone function). A function
is almost anti-monotone if it is either anti-monotone, or
there is an anti-monotone function g such that T(f) =
T(g) ∪ {1}, where 1 is the all 1’s assignment.
Every almost anti-monotone function is Horn. Now we
formulate the central concept discussed in this paper.
Definition 3 (f-complement). For Horn functions f and g
such that f ⇒ g, a Horn function h is an f-complement of g
iff f⇒
According to the definition, no complements exist if f ∼
1 (where 1 denotes the identically 1 function). Also accord-
ing to the definition, g ∼ 1 can never have a complement,
⇒
? g, if f ⇒ g but not g ⇒ f. A clause C is an
? h and f ∼ (g ∧ h).
so this case is excluded from consideration in the following
definition.
Definition 4 (Decomposable Horn function). A Horn func-
tion f is decomposable if every Horn consequence g ?∼ 1 of
f has an f-complement.
One usually works with formulas as opposed to functions,
but as the notions of complement and decomposability de-
pend only on the function represented by the formula, the
definitions are given in a syntax-independent way.
Results
For a function f and a set of variables X ⊆ U, we define the
f-closure of X to be the set of variables
Clf(X) = {v ∈ U : f ⇒ (X → v)} .
A direct consequence of this definition is that if a negative
clause C is an implicate of f, then Clf(Body(C)) = U.
In order to formulate our main result, we need two defi-
nitions. The formula ˆ ϕ is obtained from ϕ by adding to the
body of each definite clause in ϕ a variable not contained in
the closure of its body, in all possible ways.
Definition 5 (Body-building formula ˆ ϕ). For a Horn for-
mula ϕ let ˆ ϕ be the formula
?
We could have defined ˆ ϕ as a conjunction over all clauses
of ϕ, as negative clauses would make no contribution. Every
clause of ˆ ϕ is definite. It may be the case that ˆ ϕ is the empty
conjunction. This happens, for example, when ϕ consists of
negative clauses only.
GivenaHornformulaϕandaHornclauseD, wepartition
the clauses of ϕ not colliding with D into two classes.
Definition 6 (Formulas Aϕ(D) and Bϕ(D)). Given a Horn
formula ϕ and a Horn clause D, let
Aϕ(D) =
{C ∈ ϕ : C,D don’t collide, Body(D) ⊆ Clϕ(Body(C))},
Bϕ(D) =
{C ∈ ϕ : C,D don’t collide, Body(D) ?⊆ Clϕ(Body(C))}.
The existence of a complement can now be characterized.
Theorem 7 (Main theorem). Let ϕ ?∼ 1 be a Horn formula,
and ψ be a Horn consequence of ϕ. Then the following are
equivalent:
1. ψ has a ϕ-complement,
2. ˆ ϕ ?⇒ ψ,
3. for some clause D of ψ it holds that Bϕ(D) ?⇒ D.
Although the definition of ˆ ϕ is given in terms of a for-
mula, it follows from this characterization that it depends on
only the function (see also Lemma 14 below). The following
corollary gives the algorithmic aspects of Theorem 7.
Corollary 8. There is a polynomial time algorithm which,
given a Horn formula ϕ and a Horn consequence ψ of ϕ,
decides if ψ has a ϕ-complement, and if it does, then con-
structs such a ϕ-complement.
C∈ϕ definite
?
v?∈Clϕ(Body(C))
(Body(C),v → Head(C)).
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The following simple example illustrates the results.
Example 9. Let U = {x,y,z}, ϕ = C1∧ C2, where C1=
(x → y) and C2 = (y → z). Then Clϕ(x) = U and
Clϕ(y) = {y,z}. So ˆ ϕ = (x,y → z).
The clause (x,y → z) is implied by ˆ ϕ, and so it has no ϕ-
complement. This is also shown by the fact that Bϕ(x,y →
z) = {y → z}, which implies (x,y → z).
On the other hand, the clause (x → z) is not implied by
ˆ ϕ, so it does have a ϕ-complement. This is also shown by
the fact that Bϕ(x → z) = {y → z}, which does not imply
(x → z). Both constructions mentioned in the paper give
the ϕ-complement (x,z → y) ∧ (y → z).
Decomposable Horn functions have the following charac-
terization.
Theorem 10. For every Boolean function f the following
are equivalent:
1. f is a decomposable Horn function,
2. there is a Horn representation ϕ of f such that ˆ ϕ ∼ 1,
3. for every Horn representation ϕ of f it holds that ˆ ϕ ∼ 1,
4. for every Horn implicate C of f
Clf(Body(C)) = U,
5. f is almost anti-monotone.
it holds that
Proof sketch for Theorem 7
We take care of the case of negative clauses first.
Lemma 11. Let ϕ,ψ ?∼ 1 be Horn formulas such that ϕ ⇒
ψ, and ψ contains a negative clause D. Then
• ψ has a ϕ-complement,
• ˆ ϕ ?⇒ ψ,
• Bϕ(D) ?⇒ D.
Fortherestoftheproofwemayassumethatψ isadefinite
Horn formula.
The (1 ⇒ 2) part of the proof is based on the following
lemma.
Lemma 12. Let f be a Horn function and let D1= (B →
z) and D2 = (B → u) be definite Horn clauses with the
same body B such that f ⇒ D1and f ?⇒ D2. Then
D = (B,u → z)
has no f-complement.
The (2 ⇒ 3) part of the proof is omitted.
(3 ⇒ 1) part of the proof, let D be a clause in ψ such that
?
C ∈ Aϕ(D) let
χ?
C
=
z∈Body(D)
χ??
C
=(Body(C),Head(D) → Head(C)),
and finally put
C∈Aϕ(D)
For the
C∈Bϕ(D)C ?⇒ D. It can be shown that Aϕ(D) ?= ∅.
Now we can define a ϕ-complement of ψ. For each clause
?
(Body(C) → z),
χ =
?
χ?
C∧ χ??
C
∧
?
C∈(ϕ\Aϕ(D))
C
.
Thus χ is formed from ϕ by replacing clauses C ∈ Aϕ(D)
by χ?
Note that in the definition of χ??
then Head(C) = 0.
Example 13. Consider ϕ = (x → y) ∧ z and ψ = z. Then
both clauses of ϕ are in Aϕ(z), and so the ϕ-complement
of ψ provided by the construction (after deleting redundant
clauses) is (x,z → y).
Singleton Horn extensions and ˆ ϕ
A different proof of the equivalence (1 ⇔ 2) in Theorem
7 provides a semantic characterization of the body build-
ing formula. The proof is based on the following lemma.
It shows that T(ˆ ϕ) \ T(ϕ) consists of precisely the single-
ton Horn extensions of ϕ, i.e., of those points which can be
added to the set T(ϕ) maintaining the Horn property.
Lemma 14. Let ϕ be a Horn formula and a ∈ F(ϕ). Then
T(ϕ) ∪ {a} is a Horn function iff ˆ ϕ(a) = 1.
The ⇐ direction of Lemma 14 can be proved by con-
structing a Horn formula χafor T(ϕ) ∪ {a} for every truth
assignment a ∈ T(ˆ ϕ) \ T(ϕ), and this, in turn, gives an
alternative construction of complements.
Both constructions for the complement may increase the
size of the formula by a linear factor, and it is not known
whether this increase is necessary. Similar questions for
DNF are studied in (Mubayi, Tur´ an, and Zhao 2006).
C∧χ??
C, and leaving the rest of the formula unchanged.
C, if C is a negative clause
Experimental results
Recently there appears to be growing interest in exploring
the computational properties of belief revision methods by
running experiments (Benferhat et al. 2004; Bessant et al.
2001). The results presented in this paper raise the related
question of what fraction of implicates of a random Horn
formula have complements. Properties of random CNF ex-
pressions, such as their phase transition from almost surely
satisfiable to almost surely unsatisfiable have been, and are,
much studied (Martin, Monasson, and Zecchina 2001). Sim-
ilar work has also been done for random Horn formulas
(Moore et al. 2007). The results indicate that the choice of
the probability distribution on Horn formulas requires care.
We have considered the following probabilistic model
to generate a random Horn formula. The parameters are
n,m,p and q. Here n is the number of variables, m is the
number of clauses and p is the fraction of definite clauses.
For each definite clause we pick the head from the uniform
distribution over the variables. The bodies of the clauses are
generated by determining the clause length using a geomet-
ric distribution of parameter q, and then picking the right
number of variables without replacement, again using uni-
form distribution. This model produces Horn formulas with
clauses of small, but not uniformly bounded size.
After having generated a Horn formula ϕ, we used ex-
haustive testing of all implicates for having a complement.
This was done by constructing the formula ˆ ϕ, and checking
whether a candidate clause is a consequence of ˆ ϕ. By The-
orem 7, these are the implicates that do not have a comple-
ment. Because of the exhaustive testing, we present results
Page 5
20
25
30
35
40
45
50
55
60
0.1 0.2 0.30.4 0.5 0.6 0.70.80.91
Fraction of definite clauses
% of implied definite implicates
Figure 1: Percent of definite Horn implicates of ϕ implied
by ˆ ϕ as a function of p. Measured on 100 random formulas
with n = 12 variables, m = 24 clauses, and q = 1/3.
for 12 variables. As negative implicates always have com-
plements, the figures show the fraction among definite im-
plicates. For definite implicates, we want to know when we
have an exact contraction operator, and when we must turn
to some form of approximation or violation of some AGM
postulate. The computational results suggest that in certain
ranges of the parameters complements are likely to exist.
For example, Figure 2 shows that for a random formula of
50 or more clauses over 12 variables (with the clause dis-
tribution given in the figure), at least 3/4 of the implicates
have complements. It appears to be an interesting problem
to obtain theoretical results on the fractions.
20
25
30
35
40
45
1015 20 25 30354045 5055
Number of clauses
% of impled definite implicates
Figure 2: Percent of definite Horn implicates of ϕ implied
by ˆ ϕ as a function of m. Measured on 100 random formulas
with n = 12, p = 1/2, q = 1/3.
Remarks and further work
The results of this paper provide a first step towards Horn-
to-Horn belief revision, but much remains to be done. It
would be interesting to get a characterization of all com-
15
20
25
30
35
40
45
50
55
60
23456789 10 1112
Length of Implicates
% of implied definite implicates
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Figure3: PercentofdefiniteHornimplicatesofϕimpliedby
ˆ ϕ, forspecificimplicatesizes, asafunctionofimplicatesize.
Measured on 100 random formulas with n = 12 variables,
m = 24 clauses, various p, and q = 1/3.
plements, in the cases when a complement exists.
other next step is to investigate the two supplementary AGM
postulates for contraction in the Horn case; Flouris refers
to contraction operators also satisfying these postulates as
fully AGM-compliant (Flouris 2006). The problem of ex-
tending the framework of Flouris, Plexousakis, and Anto-
niou to revision, as opposed to contraction, at least in the
Horn case, is also open.But perhaps most importantly,
one should study the possibilities of approximating comple-
ments in cases when a complement does not exist, giving up
on adherence to the postulates in order to gain efficiency. As
noted in the introduction, this would constitute a departure
from current approaches to belief revision theory.
Our long term goal towards the construction of a com-
monsense reasoning agent is the integration of belief revi-
sion and learning. A commonsense reasoning agent should
not only be able to do both, but to do both efficiently. An
important motivation to study this problem is the interactive
acquisition of large commonsense knowledge bases, such as
the Open Mind Common Sense (Singh 2002) project. Here
it seems reasonable to assume that the knowledge base re-
ceives contradictory information from the users, and thus it
has to revise its contents, and at the same time should im-
prove its quality in the long run. Also, Horn logic seems
to be a reasonable knowledge representation, as inferences
needtobedonewiththeknowledgeacquired. Aninteresting
application of such knowledge bases is given by the recent
work of (Pentney et al. 2007), showing that such knowledge
bases could be combined with sensor data in health care and
other areas.
The combination of belief revision and learning has
been studied by, e.g., (Kelly, Schulte, and Hendricks 1995;
Martin and Osherson 1997; Pagnucco and Rajaratnam 2005;
Wrobel 1994). We plan to approach the problem of combin-
ing belief revision and learning in a formal model of compu-
tational learning theory. Due to lack of space, we do not de-
scribe the formal modeling details, but simply state the prob-
An-
Page 6
lem one may refer to as Knowledge Base Learning (Know-
BLe):
Problem B. Find an efficient algorithm that learns a propo-
sitional Horn formula in the model of learning from entail-
ment (with or without queries), and updates its hypotheses
in a rational manner.
Acknowledgment
This material is based upon work supported by the National
Science Foundation under Grant No. CCF-0431059. The
third author was also supported in part by the NKTH grant
of Jedlik´Anyos R&D Programme 2007 of the Hungarian
government (codename TUDORKA7).
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