Dynamic Inspiring Sets for Sustained Novelty in Poetry Generation
Pablo Gervas´
Universidad Complutense de Madrid
c/ Profesor Garc´ıa Santesmases s/n
Madrid, 28040, Spain
pgervas@sip.ucm.es
Abstract
One activity recognised as an interesting instance of
creativity is the ability of poets to systematically come
up with new poems, irrespective of how many they have
already written in the past. Poets who periodically produce
a new poem, different from the earlier ones and
comparatively valuable, are considered to embody a
more interesting type of creativity than poets who have
produced only one good poem in their lifetime, or those
that produce a succession of poems all built following
a standard recipe so obvious that it can almost be described
as a template. This paper explores the constraints
imposed on computational creative processes by
the requirement of novelty with respect to previous outputs.
Two issues emerge as fundamental: how to evaluate
novelty against a set of artifacts taken as reference,
and how to adjust construction procedures so that each
successive run leads to significantly different output. A
possible modelling of these two issues is proposed in
terms of two sets of sample artifacts: a reference set
(against which novelty is measured) and a learning set
(used to configure the construction procedures). Over
this basic model, extended discussion is carried out to
draw out interesting insights for the design of computational
creative systems.
Introduction
The process of coming up with a novel poem involves a
skill for producing something recognisable as a poem and
the ability to recognise efforts that lead to results too similar
to previous poems (in order to avoid them). Existing
automatic poetry generators have mostly focused on modelling
the generative skill rather than the historical evaluation
function. But even if these two elements were succesfully
modelled in a single system, the task of modelling how
they evolve over time would remain an important challenge
for computational creativity. Simply by generaring new material,
but also by reading material by others in between acts
of creation, human authors modify the frame of reference
that they employ to judge their own creations. Additionally,
their technique may evolve over time, sometimes through
exploration of new possibilities but quite often as a result
of a conscious effort to emulate material produced by others
that they have liked. Human authors who produce new
material by modifying their technique are considered more
creative than those that simply obtain different material using
the same technique.
The present paper reviews a number of existing poetry
generators focusing on their ability to model a generation
skill and a validation mechanism for novelty. Two issues
emerge as fundamental: how to evaluate novelty against a
set of artifacts taken as reference, and how to adjust construction
procedures so that each successive run leads to significantly
different output. A possible modelling of these
two issues is proposed in terms of two sets of sample artifacts:
a reference set (against which novelty is measured)
and a learning set (used to configure the construction procedures).
Previous Work
This section presents some useful references in terms of
computational creativity that provide a basic vocabulary to
discuss the phenomena under study, and reviews a number
of automated poetry generators.
Computational Creativity
Many efforts over the recent years that address the study of
creativity from a computational point of view acknowledge
as a predecessor the work of Margaret Boden (1990). Boden
proposed that artificial intelligence ideas might help to
understand creative thought. This idea was taken up by a
number of artificial intelligence researchers and gave rise to
a research line that attempts to model or reproduce creative
thought in computer systems. Some of Boden’s ideas have
had great influence in later work. One important idea was
the distinction between historical and psychological views
of creativity. Historical creativity (H-creativity) involves the
production of ideas that have not appeared before to any
one else in all human history. Psychological creativity (Pcreativity)
involves the production by a given person of ideas
that have not occurred before to that particular person. This
distinction is important because it implies that, unless a computer
program is given access to historical data (and generally
provided with means for social interactions with other
creators), it will only be capable of P-creativity.
Wiggins (2006) takes up Boden’s idea of creativity as
search over conceptual spaces and presents a more detailed
theoretical framework that specifies formally the different
elements involved (the universe of possible concepts, the
Proceedings of the Second International Conference on Computational Creativity 111
rules that define a particular subset of that universe as a conceptual
space, the rules for traversing that conceptual space,
the function for evaluating particular points in that space).
Wiggins points out that the rules for traversing a conceptual
space may lead to elements in the universe but outside
the definition of the conceptual space. In fact, definitions
of search space and traversal function in a creative setting
are not only particular to a given creator and different from
those used by others, but also constantly in flux.
Ritchie (2007) addresses another important issue in the
development of creative programs, that of evaluating when
a program can be considered creative. He does this by outlining
a set of empirical criteria to measure the creativity of
the program in terms of its output. He makes it very clear
that he is restricting his analysis to the questions of what factors
are to be observed, and how these might relate to creativity,
specifically stating that he does not intend to build a
model of creativity. Ritchie’s criteria are defined in terms of
two observable properties of the results produced by the program:
novelty (to what extent is the produced item dissimilar
to existing examples of that genre) and quality (to what
extent is the produced item a high-quality example of that
genre). Another important issue that affects the assessment
of creativity in creative programs is the concept of inspiring
set, the set of (usually highly valued) artifacts that the programmer
is guided by when designing a creative program.
Ritchie’s criteria are phrased in terms of: what proportion
of the results rates well according to each rating scheme, ratios
between various subsets of the result (defined in terms
of their ratings), and whether the elements in these sets were
already present or not in the inspiring set.
Jennings (2008) introduced computationally plausible
modeling of the fact that most human creativity takes place
with the creator embedded in a broader society of other creators
and critics, and that this context affects significantly
the creation of new artifacts. To capture the way in which
humans react to these constraints, Jennings defines the concept
of creative autonomy, which requires that a system be
able to evaluate its creations without consulting others, that
it be able to adjust how it makes these evaluations without
being explicitly told when or how to do so, and that these
processes not be purely random. The model he proposes relates
the evaluation of a systems creations to its perception
of how other members of its social context are likely to evaluate
them. Changes in how this evaluation is carried out may
be triggered by the need to align personal evaluations with
other members of the society or as a side effect of trying to
justify past evaluations. Creative autonomy is therefore argued
to emerge out of the interactions with multiple critics
and creators, rather than from meditative isolation.
Automatic Poetry Generators
A number of existing automated poetry generators are reviewed
focusing on the basic techniques for text creation
that have been used as underlying technologies.
The generate & test paradigm of problem solving has
been widely applied in poetry generators. Because metric
restrictions are reasonably easy to model computationally,
very simple generation solutions coupled with an evaluation
function for metric constraints are likely to produce acceptable
results (given an assumption of poetic licence as regards
to the content).
The WASP system (Gervas, 2000) draws on prior poems ´
and a selection of vocabulary provided by the user to generate
a metrically driven recombination of the given vocabulary
according to the line patterns extracted from the original
poems. The WASP automatic poet used a set of construction
heuristics obtained from formal metric constraints
to produce a poem from a set of words and a set of line patterns
provided by the user. The system followed a generate
and test method by randomly producing word sequences that
met the formal requirements. Output was impeccable from
the point of view of formal metrics, but clumsy from a linguistic
point of view, and it made little sense.
An example of poem output by WASP is given below:
Todo lo mudara la edad hermosa. ´
Marchitara la luz el vuelo helado ´
del gesto. Se escogio en color airado ´
con no hacer mudanza por su rosa.
This is a metrically correct cuarteto, a bit stilted from a
grammatical point of view, and clearly driven by the underlying
choice of vocabulary and patterns, which recall very
specific examples of Spanish sixteenth century classics. The
actual meaning1
emerges from the construction process as a
surprise.
An initial work by Manurung (1999), based on chart generation,
focuses on the generation of poetry in English, starting
from a semantic representation of the meaning of the desired
poem. A very important driving principle in this case is
to respect the unity between form and meaning that is considered
to provide the aesthetical backbone of real poetry.
This implies that poems to be generated must aim for some
specific semantic content, however vaguely defined at the
start of the composition process. The approach relied on
chart generation, taking as input a specification of the target
semantics in first order predicate logic, and a specification of
the desired poetic form in terms of metre. Words that subsume
the input semantics are chosen from a lexicon, and a
chart is produced incrementally to represent the set of possible
results. At each stage, the partial solutions are checked
semantically to ensure that no sentences incompatible with
the original input are produced. Additionally, partial results
are checked for compatibility with the desired poetic form.
Because the search space is pruned of invalid partial solutions
at each stage, the approach is generally efficient. This
corresponds to a systematic generate & test approach, trying
all possibilities and making sure that no partial constituent
is generated twice by the system. It also allows the user
to control the input in terms of meaning. This has the advantage
of restricting somewhat the probability of obtaining
non-sensical output, but it also limits the degree of freedom
of the system. The amount of creativity that system can exercise
on the semantics of its output is limited. This consti-
1Beautiful age will alter all. // The frozen flight of the gesture
// will make light wilt. // It was chosen in angry colour // on not
adapting for its rose.
Proceedings of the Second International Conference on Computational Creativity 112
tutes a significant restriction on the extent of poetic licence
allowed.
An example of poem output by Manurung’s initial system
is given below:
the cat is the cat which is dead
the bread which is gone is the bread
the cat which consumed
the bread is the cat
which gobbled the bread which is gone
This is produced by the system from input specifying
a limerick target form and target semantics {cat(c),
dead(c), bread(b), gone(b), eat(e,c,b),
past(e)} but disregarding the rhyme scheme.
Manurung went on to develop in his Phd thesis (Manurung,
2003) an evolutionary solution for this problem. Evolutionary
solutions seem particularly apt to model this process
as they bear certain similarities with the way human
authors may explore several possible drafts in parallel, progressively
editing them while they are equally valuable, focusing
on one of them when it becomes better valued, but
returning to others if later modifications prove them more
interesting. Manurung’s evolutionary solution is demonstrated
in MCGONAGALL, a proof-of-concept system for
a model of poetry generation as a state space search, solved
using evolutionary algorithms. Manurung tests his system
exhaustively, hoping to demonstrate separately its abilities
as a form aware generator (come up with poems matching
a given target form), as a tactical generator (come up with
valid realizations for a target semantics) and as a poetry
generator (combine both to come up with poems matching
the target form and the target semantics as closely as possible).
Results seem to indicate that acceptable output from
a purely linguistic point of view is easily achievable for the
form aware generator, achieved with difficulty for the tactical
generator, and extremely difficult to achieve for the poetry
generator.
An example of poem output by MCGONAGALL in its
form aware mode is given below:
They play. An expense is a waist.
A lion, he dwells in a dish.
He dwells in a skin.
A sensitive child,
he dwells in a child with a fish.
Manurung’s results explain why most automatic poetry
generators restrict themselves to operating in form aware
mode.
Another important tactic that human authors are known
to use is that of reusing ideas, structures, or phrasings from
previous work in new results. This is very similar to the
AI technique of Case-Based Reasoning (CBR). Some poetry
generators have indeed explored the use of this technique
as a basic generation mechanism. ASPERA, an evolution
of the WASP system (Gervas, 2001) used CBR to build ´
verses for an input sentence by relying on a case base of
matched pairs of prose and verse versions of the same sentence.
Each case was a set of verses associated with a prose
paraphrase of their content. An input sentence was used to
query the case base and the structure of the verses of the
best-matching result was adapted into a verse rendition of
the input. This constituted a different approach to hardening
the degree of poetic licence required to deem the outputs acceptable
(the resulting verses should have a certain relation
to the input sentence). ASPERA is described as following a
classic Retrieve-Reuse-Revise-Retain CBR cycle (Aamodt
and Plaza, 1994). The Revise-Retain stages involve carrying
out an analysis of any validated poems in order to add
the corresponding information to the system data files, to
be used in subsequent computations. The ASPERA system
requires user supervision to carry out these steps, with a human
revising successive outputs to decide which should be
retained.
An example of poem output by ASPERA is given below:
Ladrara la verdad el viento airado ´
en tal corazon por una planta dulce ´
al arbusto que volais mudo o helado.
It is interesting to observe that this poem2
shows certain
similarities to the output of the WASP system shown above
(the first verse of this terceto matches the structure of the
second verse of the cuarteto). This suggest that the same
classic verse must have been used to provide the line pattern
used by WASP and the prose-verse version of the sentence
used by ASPERA. There are also similarities in lexicon and
rhymes.
In 1984 William Chamberlain published a book of poems
called “The Policeman’s Beard is Half Constructed”
(Chamberlain, 1981). In the preface, Chamberlain claimed
that all the book (but the preface) had been written by a
computer program. The program, called RACTER, managed
verb conjugation and noun declension, and it could
assign certain elements to variables in order to reuse them
periodically (which gave an impression of thematic continuity).
Although few details are provided regarding the implementation,
it is generally assumed that RACTER employed
grammar-based generation. The poems in Chamberlain’s
book showed a degree of sophistication that many claim
would be impossible to obtain using only grammars, and it
has been suggested that a savvy combination of grammars
and carefully-crafted templates may have been employed,
enhanced by heavy filtering of a very large number of results.
An example of poem output by RACTER is given below:
More than iron
More than lead
More than gold I need electricity
I need it more than I need lamb or pork
or lettuce or cucumber
I need it for my dreams
This poem shows how structural repetition (in this instance
of constructions more than and I need) can be fundamental
for aesthetic effect.
The use of n-grams to model the probability of certain
words following on from others has proven to be another
useful tecnique. An example of poetry generation based
2
The angry wind will bark the truth // in such a heart for a sweet
plant // to the bush that you fly mute or frozen.
Proceedings of the Second International Conference on Computational Creativity 113
on this is the cybernetic poet developed by Ray Kurzweil.
RKCP (Ray Kurzweil’s Cybernetic Poet)3
is trained on a
selection of poems by an author or authors and it creates
from them a language model of the work of those authors.
From this model, RKCP can produce original poems which
will have a style similar to the author on which they were
trained. The generation process is controlled by a series of
additional parameters, for instance, the type of stanza employed.
RKCP includes an algorithm to avoid generating
poems too close to the originals used during its training,
and certain algorithms to maintain thematic coherence over
a given poem. Over specific examples, it could be seen that
the internal coherence of given verses was good, but coherence
within sentences that spanned more than one verse was
not so impressive.
An example of poem output by RKCP (after Lord Byron)
is given below:
Oh! did appear
A half-formed tear, a Tear.
By the man of the heart.
This example shows how extreme brevity can help to convey
the idea of interesting underlying semantics even when
none have actually been involved in the construction process.
Modelling Example-Driven Creation
Attempts at modelling the task of text creation computationally
tend to focus on a static representation. The conceptual
models used correspond to those of a single act of creation,
or the creation of a single text. This section outlines how a
model that considers the dynamics involved in a sequence of
acts of creation might be described in terms of some classic
concepts of computational creativity.
With a view to exploring the phenomena that we are interested
in, we need to make a few assumptions about the
various ingredients that might be involved, and how we are
to refer to them. We take up Ritchie’s terminology (Ritchie,
2007) to describe the inspiring set, the set of (usually highly
valued) artifacts that the programmer is guided by when designing
a creative program. In our case, this will be a collection
of texts that the program is aware of at the start.
However, this set of poems may be used in two different
ways. On one hand, it can be used to inform the production
mechanism that is used. Some of the possible mechanisms
for producing text used by the automatic poetry generators
reviewed above rely on a collection of text either to act as
case base, from which to extract a grammar, or on which to
train an n-gram model. We will refer to the set of texts used
for this purpose as the learning set. On the other hand, it can
be used to inform the evaluation metric used for checking
novelty of results. In most cases, this will take the form of
checking new results against this previous collection to test
for p-creativity. We will refer to the set of texts used for this
purpose as the reference set.
3
http://www.kurzweilcyberart.com/poetry/rkcp overview.php3
Instances of Inspiring Sets in Poetry Generators
ASPERA and RKCP clearly rely on a set of poems that they
use as inspiring set. In both cases this inspiring set is used
as learning set. They differ in the way in their approach to
dynamic updating of this learning set, and on whether they
use it as a reference set as well.
ASPERA includes a mention of how the results of the
system can be integrated into the creative process. Because
this system applies a CBR solution, subsequent results may
be added to the case base to provide additional material for
later runs. In this case, the set of poems used to create the
case base constitutes the learning set, in the sense that it
determines what outputs can be constructed. The ReviseRetain
stages constitute instances of expanding the learning
set. However, one must take into account the fact that this
task must be carried out by a human.
No mention is made of how ASPERA avoids replicating
partly or completely previous solutions. Indeed, in traditional
CBR systems replicating previous solutions is considered
an advantage rather than a drawback. This may be a
disadvantage when they are applied for creative purposes.
Under this light it seems fair to say that ASPERA does not
use a reference set.
In the case of RKCP, the selection of poems by a given
author from which the language model used by system is
built constitutes an instance of a learning set. RKCP introduces
an innovation by allowing the possibility of maintaining
separate models for the styles of different authors. In
this case, each one of these models comes from a different
learning set, and the system can switch from one to another
to achieve variety. Yet the set of models remains fixed.
The RKCP is said to include an algorithm to avoid generating
poems too close to the originals used during its training.
No details are provided as to how this algorithm operates.
It is unclear whether it achieves this by checking
against a set of reference texts or by constraining the construction
process in some way. Yet it is fair to say that in
this instance the learning set is also being used as a reference
set. There seems to be no mechanism for ensuring that
successive system outputs differ from one another at least as
much as they differ from the reference set.
The chart approach employed in Manurung (1999) is
aimed at ensuring that no partial constituent is generated
twice by the system. This can be seen as a different practical
solution to the problem of avoiding redundant outputs.
The use of a chart structure to store intermediate results
is another alternative to comparing each candidate with a
store of previously produced items, with no added cost of
re-indexing after each production. However, this system
applies that solution only to avoid redundancy during exploration
towards a specific single output. No mention is
made of keeping records of previous outputs to be considered
when producing new ones. For this additional purpose,
a chart would be impractical.
Dynamic Inspiring Sets as Configuration Resources
The review of existing automatic poetry generators has
shown that is is possible to define both the construction process
and the validation of output novelty with respect to a set
Proceedings of the Second International Conference on Computational Creativity 114
of texts, referred to as inspiring set. The concept of an inspiring
set of poems as means of configuring a poetry generator
presents the advantages of corpus-based approaches to
natural language processing. Within this paradigm, the performance
of a processing system is determined by the corpus
on which it was trained. If the system needs to change, this
is done by changing the corpus and retraining the system.
In computational work carried out so far on automated poetry
generators, inspiring sets have always been configured
in a static way. A given set of texts is taken as inspiring set
and used as learning set and/or reference set, with smaller or
larger numbers of poems produced with no thought to how
the act of producing new poems might affect either. Ideally
we would want to explore the possibilities of having the
creation process itself provide feedback to these sets, with
a view to identifying whether such feedback might capture
some of the patterns observed in human creativity. If this
approach is applied in creative endeavours, it presents the
additional problem of requiring some means for managing
the progressive evolution of these inspiring sets, including
the need to periodically retrain the system. This issue is discusses
with respect to the technical solutions outlined earlier.
CBR approaches include the means for systematically updating
the case base, which constitutes an instance of a learning
set. A case base can also be employed as a reference set
if the retrieval stage is configured to always select slightly
dissimilar cases, forcing the reuse stage to apply heavy adaptation.
In a similar vein n-gram based approaches provide means
for enforcing difference with the inspiring set by controlling
the probability of the resulting chains. To model the possibility
of progressively extending the inspiring set, a procedure
would have to be introduced for periodically retraining
the models employed.
The Problem of Managing Inspiring Sets
From the point of view of collecting intuitions on the various
challenges involved in modelling human creativity, the
issues outlined so far point to a candidate requirement that
had not been identified as part of creative systems developed
in the past: the need for appropriate management of the set
of inspiring artifacts.
Where these artifacts are used as a reference set, appropriate
management of the reference set would become a must
for any system that intends to be aware of whether its results
in successive runs are indeed new with respect to previous
output. This would correspond to the way in which human
creators seem to be aware of the state of the art in their fields,
at least to the extent of knowing when solutions being explored
are indeed new. This requirement would be tightly
related to Boden’s concept of p-creativity.
The most straightforward solution to apply would be to
test every result for similarity against previously known
texts. This requires each new result to be compared with an
ever increasing set of previous results. Elaborate means of
indexing or clustering the set of reference texts would help
reduce the complexity of checking for novelty. However,
the improvements obtained in such a way would have to be
offset with the difficulties involved in having to re-index the
reference set each time a new result is added.
From an engineering point of view, it would make sense
to resort to indexing the set of reference artifacts. The task of
indexing would slow down the production of successive results,
but it would speed up the task of exploring the conceptual
space in search for new candidate solutions. This would
match the intuition that human creators may take some time
between the creation of one piece and setting out to produce
a new one. During this time a creator may be digesting the
results of his last creation, or searching for inspiration for
the next. The task of re-indexing the reference set could
be identified as part of either or both of these processes. In
contrast, during the act of creation itself human creators shift
very rapidly through candidate solutions, with no lingering
on past material. This would match more closely an engineering
model based on prior indexing than one based on
systematic comparison.
Where the set of already known artifacts is used to inform
the construction procedure, different procedures will
arise depending on the particular technique employed. In
CBR solutions, management of the learning set would involve
periodically re-indexing a growing case base. For
n-gram based approaches, management of the learning set
would consist of periodically retraining the set of models
employed, or possibly more radical periodical rearrangements
involving re-clustering of the learning set and training
of a new set of models based on the resulting set of clusters.
With respect to human performance, the periodical maintenance
of the learning set would mirror evolution of author’s
technique.
Intuitions for Computational Creativity
The issue of considering successive system results as part
of the reference set is fundamental for being able to discern
whether any given result is p-creative as defined by Boden.
Not many previous creative systems consider this. A notable
exception is the MEXICA storytelling system (Perez y ´
Perez, 1999), which does include a mechanism for checking ´
its results with previous outputs.
The subdivisions of the inspiring set described above
could be related to some of the formal elements described in
Wiggins’ framework (Wiggins, 2006). The learning set of
any such system actually determines the conceptual space,
and the production mechanism derived from it constitutes
the operational equivalent of a traversal function. In a similar
vein, the reference set will undoubtedly play a fundamental
role in any evaluation function designed to consider
novelty, as that of any system aiming to be creative should.
The possibilities discussed above of dynamically updating
either the learning set or the reference set would capture the
intuitions that definitions of search space, traversal function
and evaluation function may be constantly in flux.
The reference set in its various configurations described
above plays an important role in the definition or identifi-
cation of the novelty of the results of a program that aims
at being creative, as described by Ritchie (2007). No mention
has been made in this paper of the quality of the results,
which is their other important observable property. An apProceedings
of the Second International Conference on Computational Creativity 115
propriate evaluation of the quality of results would play a
fundamental role in all the processes described above. Only
results that have been rated above a given threshold of quality
should be considered for updating either the reference
set or the learning set. This constitutes a less obvious but
quite significant role of whatever evaluation function is used
to determine quality in a creative system. If the most obvious
role of the quality function is to select actual system
outputs, by doing so it actually guides the development of
an individual style for the system, through the task of filtering
what results get added to reference or learning set. The
choices taken in the past are fed back into system operation
and affect choices taken in the future.
The dynamic handling of the reference set and the learning
set may provide a simple way of implementing in computational
systems the kind of social interaction described
by Jennings as fundamental for human creativity (Jennings,
2008). Systems defined in terms of dynamic inspiring sets
as described in this paper would automatically adjust their
novelty evaluation functions just by being exposed to new
artifacts as they are added to the reference set. They would
also adjust their production mechanisms by exposure to new
artifacts as they are added to their learning set. This would
allow for easy modelling of the interplay between learning
to emulate the work of creators one admires (as driven by its
inclusion in the individual’s learning set) and trying to steer
away from the work one dislikes (by including it in the reference
set). Such a model would capture much of the way in
which human creators learn their trade.
Conclusions
Computational approaches to creativity in the past have
mostly considered static views of the creative process, in the
sense that they have focused on a single act of creation rather
than what it is that enables a creator to achieve a sequence
of acts of creation, each one producing a different artifact
that innovates with respect to previous ones and possibly is
the result of a different process of construction or composition.
The present paper has explored how such a dynamic
view of the creative process may be modelled in terms of
two subdivisions of the concept of inspiring set: a learning
set from which the production mechanisms are learnt, and
a reference set against which candidate results have to be
checked to ensure novelty. This approach implies that each
individual creative act must be considered in a larger context.
From an engineering point of view this presents the
problem of how to manage the reference set in a computationally
tractable manner. It also introduces the problem of
when and how each of these sets is updated, or when they are
processed to re-train the production mechanisms or re-index
the reference set.
As future work it would be very valuable to consider possible
ways of implementing some of these ideas in experimental
systems. Such systems need not attempt to model
full-blown literary generators. Even the simpler task of generating
coherent text as output could serve as a test for some
of the ideas presented in this paper. The mechanisms for ensuring
novelty with respect to a set of source materials apply
just as well to simple text as to more literary efforts. The
ability to identify when a production mechanism is reaching
its limits and begins to produce less novel material can also
be tested over such simple set ups.
Acknowledgments
The research reported in this paper is partially supported
by the Ministerio de Educacion y Ciencia ´
(TIN2006-14433-C02-01, TIN2009-14659-C03-01), Universidad
Complutense de Madrid and Direccion General de ´
Universidades e Investigacion de la Comunidad de Madrid ´
(CCG07-UCM/TIC 2803).
<references_biblio/>
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