Using Argumentation to Evaluate Concept Blends in Combinatorial Creativity
Roberto Confalonieri1, Joseph Corneli2, Alison Pease3, Enric Plaza1 and Marco Schorlemmer1
1Artificial Intelligence Research Institute, IIIA-CSIC, Spain 2Computational Creativity Research Group, Department of Computing, Goldsmiths, University of London, UK 3Centre for Argument Technology, School of Computing, University of Dundee, UK
Abstract
This paper motivates the use of computational argumentation
for evaluating ‘concept blends’ and other forms of combinatorial
creativity. We exemplify our approach in the domain of
computer icon design, where icons are understood as creative
artefacts generated through concept blending. We present a
semiotic system for representing icons, showing how they
can be described in terms of interpretations and how they
are related by sign patterns. The interpretation of a sign pattern
conveys an intended meaning for an icon. This intended
meaning is subjective, and depends on the way concept blending
for creating the icon is realised. We show how the intended
meaning of icons can be discussed in an explicit and
social argumentation process modeled as a dialogue game,
and show examples of these following the style of Lakatos
(1976). In this way, we are able to evaluate concept blends
through an open-ended and dynamic discussion in which concept
blends can be improved and the reasons behind a specific
evaluation are made explicit. In the closing section, we explore
argumentation and the potential roles that can play at
different stages of the concept blending process.
Introduction
A proposal by (Fauconnier and Turner, 1998) called concept
blending has reinvigorated studies trying to unravel the general
cognitive principles operating during creative thought.
According to (Fauconnier and Turner, 1998), concept blending
is a cognitive process that serves a variety of cognitive
purposes, including creativity. In this way of thinking, human
creativity can be modeled as a blending process that
takes different mental spaces as input and blends them into
a new mental space called a blend. This is a form of combinatorial
creativity, one of the three forms of creativity identified
by Boden (2003). A blend is constructed by taking
the existing commonalities among the input mental spaces
(called the generic space) into account, and by projecting the
structure of the input spaces in a selective way. In general
the outcome can have an emergent structure arising from a
non-trivial combination of the projected parts. Different projections
lead to different blends and different generic spaces
constrain the possible projections.
This poses challenges from a computational perspective:
large number of possible combinations exhibiting vastly different
properties can be constructed by choosing different
input spaces, using different ways to compute the generic
space, and selecting projections. Within the Concept Invention
Theory project1 (COINVENT), we are currently developing
a computational account of concept blending based on
insights from psychology, Artificial Intelligence (AI), and
cognitive modelling (Schorlemmer et al., 2014). One of our
goals is to address this combinatorial nature. One potential
outcome of this work is a deeper understanding of the way
combinatorial creativity works in general.
The formal and computational model for concept blending
under development in COINVENT (Bou et al., 2014)
is closely related to the notion of amalgam (Ontan˜on and ´
Plaza, 2010). Amalgamation has its root in case-based reasoning
and focuses on the issue of combining solutions coming
from multiple cases. Assuming the solution space can be
characterised as a generalisation space, the amalgam operation
combines input solutions into a new solution that contains
as much information from the two inputs solutions as
possible. When input solutions cannot be combined, amalgamation
generalises them by dropping some of their properties.
This process of generalisation and combination can
be expensive from a computational point of view, depending
on the search space to be explored.
The amalgam-based approach for computing blends
makes explicit the combinatorial nature of concept blending,
which raises the issue of evaluating and selecting novel and
valuable blends as opposed to those combinations that lack
interest or significance. Although Fauconnier and Turner
(1998) suggest a number of qualitative criteria that can be
used for evaluating concept blends, it is not straightforward
to chararacterise them in a computational model.
In this paper, we propose to explore an argumentative approach
to understanding and evaluating the meaning, interest,
and significance of concept blends. Specifically, we propose
to view evaluating blends as a process of argumentation,
in which the specifics of a blend are pinpointed and
opened up as issues of discussion. Our intuition is that in
the context of new ideas, proposals, or artworks, people use
critical discussion and argumentation to understand, absorb
and evaluate. We also consider the constructive roles that
argumentation can play in concept blending.
Computational argumentation models have recently appeared
in AI applications (Bench-Capon and Dunne, 2007;
1
See http://www.coinvent-project.eu for details.
Proceedings of the Sixth International Conference on Computational Creativity June 2015 174
I1 I2
¯I2 ¯I1
B
G
Figure 1: An amalgam diagram with inputs I1 and I2 and
blend B obtained by combining ¯I1 and ¯I2. The arrows indicate
generalisation.
Rahwan and Simari, 2009), and we believe that incorporating
argumentation can foster the development of a fuller
computational account of combinatorial creativity. The current
paper develops these themes at the level of (meta-)
design; implementation is saved for future work.
Roles of Argumentation in Concept Blending
Consider the amalgam diagram modeling the concept blending
process (Figure 1): two input spaces I1, I2, two of their
possible generalisations ¯I1, ¯I2, which have a generic space
G and blend B. When two input spaces cannot be combined
because they do not satisfy certain criteria, the inputs have
to be generalised for omitting some of their specifics. The
combination of each specific pair ¯I1, ¯I2 yields a blend.
Informally, we can imagine argumentation taking place
at various points in the amalgam diagram. In general this
would happen in response to indeterminacy, that is, when
some features of the diagram are underdetermined. We foresee
that argumentation can be used:
a. to express opinions or points of view that can be used for
guiding the selection/omission of specific parts of the input
spaces; in particular, to select a specific pair of generalisation
¯I1, ¯I2 of the input spaces in the blending process;
b. to provide a computational setting for modeling discussions
around the quality of a creative artefact, with the
aim of evaluating and refining the generated blends.
In the first case, arguments would be about generalisation,
i.e. which features should be preserved from I1 and which
features should be preserved from I2. More complex inferences
could be involved, for example in a case where I1 is
fixed, and constraints and various optimality criteria on the
blend are imposed, which then yield various constraints on
what the other input I2 should be. We return to this point in
the discussion section, and we focus for the most part on the
second case.
In the second case, argumentation would be used to evaluate
a range of blends, and the evaluation is carried out post
hoc, by a variation of try-it-and-see. A range of blends are
trialled, each one bringing out different (un)intended meanings.
The evaluation is modeled as an argument, or dialogue
in which the specifics of a blend are pinpointed and opened
up as issues of discussion. This dialogue can be considered
as an introspective evaluation, although it usually takes place
among several parties as a means for the social development
and understanding of creative artefacts. In this paper, we
focus on this role.
Our Approach
To exemplify our approach, we take the domain of computer
icons into account. We assume that concept blending is the
implicit process which governs the creative behavior of icon
designers who create new icons by blending existing icons
and signs. To this end, we propose a simple semiotic system
for modeling computer icons. We consider computer
icons as combinations of signs (e.g. document, magnifying
glass, arrow etc.) that are described in terms of interpretations.
Interpretations convey actions-in-the-world or concepts
and are associated with shapes. Signs are related by
sign-patterns modeled as qualitative spatial relations such
as above, behind, etc. Since sign-patterns are used to combine
signs, and each sign can have multiple interpretations,
a sign-pattern used to generate a computer icon can convey
multiple intended meanings to the icon. These are subjective
interpretations of designers when they have to decide
what is the best interpretation an icon can have in the real
world. In this paper, we show how the intended meaning of
new designed (blended) icons can be evaluated and refined
by means of Lakatosian reasoning.
Background
Computational argumentation
Computational argumentation in AI aims at modeling the
constitutive elements of argumentation, that are i) arguments,
ii) attack relations modeling conflicts, and iii) acceptibility
semantics for selecting valid arguments (BenchCapon
and Dunne, 2007; Rahwan and Simari, 2009).
The most well-known computational argumentation
framework is due to Dung (1995). Dung defines an abstract
framework to represent arguments and binary attack relations,
modeling conflicts, by means of a graph. He defines
different acceptibility semantics to decide which arguments
are valid and, consequently, how conflicts can be resolved
(Figure 2).
a1 a2 a3
Figure 2: Dung framework example: Nodes represent arguments
and edges (binary) attack relations. Argument a1 is
attacked by a2 which is attacked by a3. Thus, a2 is defeated
and a1 can be accepted. a3 is also accepted.
Abstract argumentation frameworks do not deal with how
arguments are generated and exchanged. They merely focus
on attack relations between arguments and acceptibility
semantics. However, the intrinsic dialectical nature of argumentation
is fully explored when an explicit argumentation
process is considered. Then, the purpose of a dialogue becomes
essential to determine how arguments should be generated
and exchanged, and how a dialogue should be structured
(Walton and Krabbe, 1995).
Lakatosian argument and dialogue
Lakatos (1976) was a philosopher of mathematics who developed
a model of argument, presented as a dialogue, to
Proceedings of the Sixth International Conference on Computational Creativity June 2015 175
Accept
Exception-bar/
Monster-bar/
Monster-adjust
Surrender
Counterexample
Conjecture
Figure 3: Our interpretation of Lakatos’s game patterns.
describe ways in which mathematicians explore and develop
new areas of mathematics. In particular, he looked at the
role that conflict plays in such explorations, presenting a rational
reconstruction of a dialogue in which claims are made
and counterexamples are presented and responded to in various
different ways. His resulting model describes conceptual
continuity and change in the growth of knowledge, and
contains dialogue moves, or methods, which suggest ways
in which concepts, conjectures and proofs are fluid and open
to negotiation, and gradually evolve via an organic process
of interaction and argument between mathematicians. These
dialogue moves are:
Surrender consists of abandoning a conjecture in the light
of a counterexample.
Piecemeal exclusion is an exception-barring method that
deals with exceptions by excluding a class of counterexamples,
i.e., by generalising from a counterexample to a
class of counterexamples which have certain properties.
Strategic withdrawal is an exception-barring method that
uses positive examples of a conjecture and generalises
from these to a class of object, and then limits the domain
of the conjecture to this class.
Monster-barring/monster-adjusting is a way of excluding
an unwanted counterexample. This method starts with
the argument that a ‘counterexample’ can be ignored because
it is not a counterexample, as it is not within the
claimed concept definition. Rather, the object is seen as a
monster which should not be allowed to disrupt a harmonious
conjecture. Using this method, the original conjecture
is unchanged, but the meaning of the terms in it may
change. Monster-adjusting is similar, in that one reinterprets
an object in such a way that it is no longer a counterexample,
although in this case the object is still seen as
belonging to the domain of the conjecture.
The moves above are not independent processes; much of
Lakatos’s work stressed the interdependence of creation and
justification. These moves describe the evolution of both arguments
and conclusions in mathematics, and as such constitute
argument patterns, or schemes. However, they are
a rational representation of exchanges between mathematicians
and describe dynamic, rather than static arguments,
presented as a dialogue. Thus, they also have temporal structure,
and can be seen as a dialogue game, in which at any
point various dialogue moves are applicable (see (Pease et
al., 2014) for a description of Lakatos’s methods in these
terms). The fact that we include negotiations over definitions
and changes in the conclusions being argued means that it is
difficult to apply traditional abstract argumentation frameworks,
which assume that such aspects are stable. However,
we can see some of the moves in terms of Dung’s framework:
for instance if an initial argument for a conjecture
forms a1 in Figure 2, then a2 might be a counterexample to
the conjecture, and a3 might be the monster-barring move.
The Lakatosian way of conceiving the reasoning as an
open-ended discussion about a problem suggests that we
can exploit Lakatos’s moves for structuring dialogues for
the evaluation of creative artefacts. Evaluation in creativity
is not a static and rigid process, and the discussion should
flow in a dynamic way. As such, in this paper, we propose
to use Lakatosian reasoning to model the negotiation about
the intended meaning of generated blends (icons). Figure 3
shows the dialogue game we will adopt to model these dialogues.
For another formal framework of dialogue games
for argumentation see (Prakken, 2005).
Icons and Signs
We follow a semiotic approach to specify the intended meaning
of computer icons. Semiotics is a transdisciplinary approach
that studies meaning-making with signs and symbols
(Chandler, 2004). Although it is clearly related to linguistics,
semiotics also studies other forms of non-linguistic sign
systems and how they may convey meaning; this includes
not only designation, but also analogy, and metaphor. Although
some people may regard Peirce’s Sign Theory as the
origin of semiotics, Saussure founded his semiotics (semiology)
in the social sciences. Currently, cognitive semiotics
and computational semiotics take their own perspectives on
the relation between sign and meaning-making. In this paper,
we take a semiotic approach to describe computer icons
in the sense that icons, as a spatial pattern of shapes, are
viewed as signs, and compositions of signs are interpreted
to convey a meaning, as when we say ‘this icon means the
download is still active’.
The shapes recurrently used in icons are interpreted as
signs; screens, magnifying glasses and folders are examples
of signs. A magnifying glass sign can be used in different
icons in such a way that its meaning is context-dependent,
that is, it depends on other signs related to it in different
icons. We associate to each sign a set of interpretations, that
encode the kinds of intended meaning associated to that sign
as actions-in-the-world or concepts.
An icon is represented as a pattern defined by a collection
of signs and qualitative spatial relations like above, behind,
etc. We can find patterns of meaning that are shared among
different icons by analysing recurrent patterns of signs and
their spatial relation. We call them sign patterns. A sign
pattern has an associated collection of interpretations that
encode the intended meanings associated to that sign pattern.
Signs, sign patterns, and interpretations, which we will
use in the paper, can be built by analysing and annotating
existing libraries of computer icons. As we shall see, the inherent
polysemy of signs, sign patterns and icons opens the
way to use arguments for evaluating the quality or adequacy
of new icons created by concept blending.
Proceedings of the Sixth International Conference on Computational Creativity June 2015 176
Shapes
Sign ID
Intepretations
Document
{info-container, document, text, page, file}
SIGN
(I) The structure of the DOCUMENT sign, including associated
shapes and interpretations.
Shapes
Sign ID
Intepretations
MagnifyingGlass
{examine, analyse, preview, search, find-in}
SIGN
(II) The structure of the MAGNIFYINGGLASS sign, including
associated shapes and interpretations.
Figure 4: Example of signs
(a) (b) (c) (d)
Up
Down
Arrow
X
(I) (a) the sign pattern FROM-DOWNARROW with three examples of
the pattern where X is a sign for (b) cloud content, (c) document content,
and (d) audio content.
(a) (b) (c)
Down
Arrow
X
Up
(II) (a) the sign pattern DOWNARROW-TOWARD with
two examples of the pattern where X is a sign for (b) a
hard disk storage, and (c) an optical disk storage.
Figure 5: Example of different sign patterns used with the same sign DOWNARROW
A semiotic system for icons
In this section, we will formalise the notions presented
above. A sign S is a tuple hid, F, Ai where id is a sign identifier,
F is a set of shapes embodying the sign S and A is a
set of interpretations. We use S to denote the available set
of signs. Figure 4 provides two examples. Figure 4I shows
the structure of the DOCUMENT sign, with several shapes
embodying the sign, and a list of interpretations that express
how this sign is used in different ways to convey meanings
such as info-container, document, text, page, file. Intuitively,
this means that the shapes used in the icons are sometimes
interpreted as a document and other times as a page, etc.
Moreover, the specific shapes can be used interchangeably to
embody a DOCUMENT, i.e. there is no clear distinction, regarding
the shapes, between document vs. page vs. file. Another
example of a sign is the MAGNIFYINGGLASS shown
in Figure 4II, with interpretations examine, analyse, preview,
search, and find-in.
We will also describe a library of annotated icons I,
where each icon I 2 I consists of two parts: (1) a spatial
configuration of signs and (2) the intended meaning of that
icon. For instance, in Figure 5I, the icon (b) has the spatial
configuration of a ‘cloud on top of a downward-arrow’ and
its meaning is ‘downloading content from the cloud’.
Sign patterns
In our framework, sign patterns relate signs in icons using
spatial qualitative relationships such as above, behind, up,
down, left, etc. We assume that these relationships are represented
as binary predicates, Above(X,Y), Up(X,Y), etc.,
where X and Y are variables ranging over signs in S. For
our current purposes, we use the qualitative spatial relationships
defined in (Falomir et al., 2012).
Let us consider two examples of sign patterns that include
the DOWNARROW sign. DOWNARROW has a vertical
downward-pointing arrow shape and is associated with the
interpretations {down, downward, downloading, downloadfrom
and download-to}. The sign pattern called FROMDOWNARROW
(shown in the schema labelled (a) in Figure
5I) uses the qualitative spatial relationship up between
a variable X and the sign DOWNARROW. Examples (b),
(c) and (d) in Figure 5I illustrate the intuitive meaning of
the sign pattern FROM-DOWNARROW: ‘downloading X’.
Thus, example (b) refers to downloading cloud content, (c)
document content, and (d) audio content.
The inherent asymmetry of arrows in general, and arrow
signs particularly, can be appreciated when considering the
opposite spatial relation, when the sign DOWNARROW is
“up” from another sign (Figure 5II). Then, the sign pattern
DOWNARROW-TOWARD is used to mean that X is the destination
of the downloading. Example icons (b) and (c) are
intended to mean that the data being downloaded (whose
type or origin is now elided) is to be stored in a destination
such as a hard disk or an optical disk.
Evaluating Blends using Argumentation
As briefly described previously, the amalgam-based computation
of concept blending amounts to combine different input
spaces into a new space, called blend, by taking the commonalities
of the inputs into account, by generalising some
of their specifics and by projecting other elements. In the
following, we describe how concept blending can account
for modeling the creative process of a designer of computer
icons.
Proceedings of the Sixth International Conference on Computational Creativity June 2015 177
Above(MagnifyingGlass,HardDisk)
Above(MagnifyingGlass,Y)
Generalisation
Combination
Above(MagnifyingGlass,Document)
Preview Page
Interpretation: Search-HardDiskContent
Interpretation : Preview-Page
Blend
Input 1
Above(Pen,Document)
Interpretation: Edit-Document
Input 2
Above(X,Document)
Above(X,Y)
Generalisation
Generic Space
Figure 6: Generating an icon interpreted as Preview-Page
through amalgam-based concept blending.
A design scenario
Assume a designer is looking for creating a new icon with
the intended meaning of previewing a document or a page.
The creation of such icon can be achieved by the following
amalgam-based concept blending process (Figure 6). In addition
to the DOCUMENT and MAGNIFYINGGLASS signs,
we assume we have available a HARDDISK sign and a PEN
sign which have already been used to make icons.
The input mental spaces. The input mental spaces
of the designer are an icon of a hard-disk with a magnifying
glass hovering above it, whose meaning is
Search-HardDiskContent, and an icon of a document
with a pen above it, whose meaning is Edit-Document.
The generic space. The sign pattern Above(X,Y)
is used in both icons. The first icon contains
the relation Above(MAGNIFYINGGLASS, HARDDISK)
between the MAGNIFYINGGLASS and the HARDDISK,
and the second contains the relation Above
(PEN,DOCUMENT) between the PEN and the DOCUMENT.
Further generalisation. Two generalisation steps
are needed: Above(MAGNIFYINGGLASS,HARDDISK)
! Above(MAGNIFYINGGLASS,Y); correspondingly,
Above(PEN,DOCUMENT) ! Above(X,DOCUMENT).
Combination via variable substitution. We combine
the schemas Above(MAGNIFYINGGLASS, Y) and
Above(X, DOCUMENT) via [X/MAGNIFYINGGLASS,
Y/DOCUMENT]. The icon of a page with a magnifying
glass hovering above it is generated.
The intended meaning. The designer associates to
the icon the intended meaning of Preview-Page, by
selecting the interpretations (Preview, Page) for the
MAGNIFYINGGLASS and DOCUMENT signs.
In this case, the designer decided that the intended
meaning of Above(MAGNIFYINGGLASS, DOCUMENT) is
Preview-Page, that is, a page can be examined without opening
it. However, during the creative process, the designer
could have generated other blends, not only by combining
other signs, but also by selecting different interpretations
associated to the MAGNIFYINGGLASS and DOCUMENT
signs. For instance, the icon in Figure 7 still represents
a page with a magnifying glass hovering above it, but
it has been given a different intended meaning.
Find-in-Page Magnifying
Glass Document Object Level Above
Interpretation Level Find-in Page
Figure 7: An example of interpreting the sign pattern of an
icon as Find-in-Page.
The meaning of a blended icon cannot simply be considered
right or wrong: interpretation depends on different points of
view. Thus the evaluation of whether it is useful or valid for
a specific purpose can be the object of a discussion.
Arguments about intended meanings
In the icon domain, arguments may include a clear interpretation
of any constituent signs in the icon if it is a composition
of signs, or a good fit with other icons in the icon set.
For example, we can consider a counter-argument, i.e. an
argument that attacks the interpretation a1 “magnifying
glass above document means Preview-Page” in Figure 6, to
be phrased as follows:
a2 : “However, the icon in Figure 6 can also be interpreted
to mean Find-in-Page.”
The rationale is that the MAGNIFYINGGLASS sign can often
be understood as finding or searching for something. Thus,
the icon can be also interpreted as Find-in-Page by associating
the interpretation find-in instead of preview for the same
sign MAGNIFYINGGLASS (Figure 7).
This attacking argument can be made at an abstract/conceptual
level, for instance, by taking other possible
blends of the DOCUMENT and MAGNIFYINGGLASS signs
related by the sign pattern Above(X,Y) into account. Or, alternatively,
if there is an icon library that contains an icon
that ‘satisfies’ the argument above, then this attacking argument
can be supported by a specific counterexample. Any
of these two forms of attack evaluates negatively the icon in
Figure 6. Therefore, if there are several alternative designs
for a new icon, this attacking argument diminishes the degree
of optimality/adequacy of that design with respect to
alternative designs.
The original interpretation can be defended, as usually
done in computational argumentation models, by a new argument
that attacks the attacking argument a2. For instance,
the designer may say:
a3 : “The icon in Figure 6 can only be interpreted differently
if MAGNIFYINGGLASS is understood to mean
find-in instead of preview. However, the other icons in
Proceedings of the Sixth International Conference on Computational Creativity June 2015 178
my library use MAGNIFYINGGLASS to mean preview,
not find-in.”
Argumentation semantics can then be used, once a network
of arguments is built, to determine the outcome. For instance
whether argument a1, the original interpretation, is defeated
or not can be determined as follows (Figure 2): in this example
a3 has no attack, so it is undefeated, which means
it defeats a2; since a2 is defeated, the attack against a1 is
invalid and a1 is undefeated (i.e. is accepted).
Arguments about the intended meanings of an icon can
be embedded in a dialogue modeled in terms of Lakatos’s
moves and the dialogue pattern shown in Figure 3.
Lakatosian reasoning for blend evaluation
Here we present a Lakatos-style dialogue between two players,
a proponent P and an opponent O. The goal of each
player is to persuade the other player of a point of view, in
this paper, the intended meaning of a new blended icon. In
such a setting, we expect to see negotiations over the meaning
of an icon take place between experts and novices, or
between people designing icons and people using (interpreting)
them, or various combinations.
To discuss a given icon using Lakatosian reasoning, we
assume that an initial conjecture is about the interpretation
of an icon usually being an action-in-the-world or a concept,
together with an example of a particular icon and a particular
interpretation. The conjecture could be constructed by
inductive generalisation.
Example 1. In this example, Lakatosian reasoning is used
for discussing the intended meaning of a new icon generated
by concept blending:
P1: “An icon with a magnifying glass over a page means
Preview-Page” (Conjecture)
O1: “I disagree, this icon (Figure 7) means Find-in-page.”
(Counterexample)
P2: “No, this is a different case because the magnifying
glass must be over pages with text on them to magnify (it
shows what we’re about to magnify).” (Monster-barring)
After this dialogue, it is agreed that the intended meaning of
the icon is Preview-Page and the icon itself has been clarified.
Alternatively, the proponent and the opponent could
make a different evaluation by following different moves.
For instance, if the proponent accepts the counterexample,
then the intended meaning of the icon can be refined due to
piecemeal exclusion:
P1: “An icon with a magnifying glass over a page mean
Preview-Page” (Conjecture)
O1: “I disagree, this icon (Figure 7) means Find-in-page.”
(Counterexample)
P3: “Ok, only icons with a magnifying glass over a page
with text mean Preview-Page”. (Piecemeal exclusion)
After this dialogue the intended meaning about the new icon
has been changed by modifying the conjecture and taking
the counterexample into account.
Sometimes players have different points of view due to
the sign patterns they have used in their concept blending.
(I) Composite cloud icon (II) Stateful component (III) Processing component
Figure 8: Interpreting the design of cloud icons2
Example 2. Let us imagine that the proponent has generated
an intended meaning for an icon using the FROMDOWNARROW
sign pattern, whereas the opponent has used
the DOWNARROW-TOWARD pattern (Figure 5 illustrates
these cases). The two players can engage in the following
dialogue:
P1: “Look at icons in Figure 5I, icons with a DOWNARROW
relate to content.” (Initial Conjecture)
O1: “The icon in Figure 5IIb has a DOWNARROW but
doesn’t relate to content.” (Counterexample)
O2: “The icon in Figure 5IIc also has a DOWNARROW but
doesn’t relate to content.” (Counterexample)
P2: “The conjecture is right because the two examples actually
do relate to content as they are to do with storage
and content is part of storage.” (Monster-adjusting)
In this case, the proponent excludes the counterexamples using
monster-adjusting, and reinterpreting them in a way that
they are not counterexamples anymore.
A conjecture might even be at a higher level, for asserting
that a particular metaphor is appropriate or inappropriate.
Example 3. For example, someone who is familiar with the
‘gear means adjust setting’ metaphor in one program may be
comfortable with it in another program:
P1: “ An icon containing the ‘gear’ sign is a good one for
Settings, because it invokes the idea of a gear change on a
bicycle” (Initial Conjecture)
O1: “The ‘gear’ sign does not invoke the idea of a gear
change on a bicycle from my point of view.” (Counterexample)
P2: “Ok, you’re right, it does not invoke the idea of a gear
change on a bicycle, but it is often used for Settings.”
(Monster-adjusting)
Example 4. Argumentation may also consider the role a
given abstract design plays within a given icon set.
P1: “Even without knowing what the first or third icon in
Figure 8I stands for, I can make a conjecture that it has
to do with a server or a user interface accessed via the
cloud. However, with the second icon, I’m not sure what
it means. It is composed of various signs that I don’t understand.
It’s probably badly designed.” (Conjecture)
O1: “Did you notice that icons in Figure 8II and Figure 8III
are both defined as part of the same icon set? They
mean ‘Stateful component’ and ‘Processing component’
respectively. Therefore, the second icon is actually well
designed, because it uses signs appearing in other icons
of the same icon set.” (Counterexample)
2
From http://cloudcomputingpatterns.org.
Proceedings of the Sixth International Conference on Computational Creativity June 2015 179
P2: “But the second icon contains a pipe sign that is not
used anywhere within the icon set, so I still don’t know
what the second icon means. If there were an icon with
a pipe sign with a clear meaning, then I could understand
the second icon better. ” (Strategic withdrawal)
The main characteristic of employing Lakatosian reasoning
is that it allows a dynamic and social development of the intended
meaning of blended icons. This cannot be achieved
by using only abstract argumentation frameworks, since
they assume that the object of discussion does not evolve.
Therefore, having an argumentation process of this kind has
several advantages: it promotes not only open-discussions
around the meaning of an icon, but also the construction of
a discourse about how an intended meaning is obtained.
This is a desirable characteristic in computational creativity
when evaluating creative outcomes such as concept
blends. In this way, the evaluation evolves into a refinement
process of an initial created concept, giving much more flexibility
at the moment of deciding whether a blend is suitable.
Discussion
We have illustrated the use of argumentation to evaluate
completed blends. We alluded earlier to the role argumentation
can play in the generation of blends, for instance by suggesting
different ways to generalise the input spaces. Indeed,
successive statements may serve to carry out the steps in the
blending process iteratively, relaxing or refining as needed.
These steps can be modelled using Lakatos’s moves. From a
conjectural candidate solution, to additional criteria that reveal
this blend to be a ‘monster’ (i.e. which identify features
of the candidate solution that cannot be allowed in the final
solution for one reason or another), to adjustments that yield
a more complete description of the problem and point the
way toward a more satisfactory solution. An example of using
argumentation for deciding which generalisations to use
for creating a new icon is the following:
A: “We can create a different blend icon starting from the
same icons of before.” (see Figure 6)
B: “We could use the HARDDISK sign from the first icon
and the DOCUMENT sign from the second icon.”
A: “But putting the DOCUMENT sign above the HARDDISK
does not make sense from my point of view.”
B: “You’re right, let’s use the HARDDISK sign from the first
icon and the PEN sign from the second icon.“
A: “Sounds good, now we have a Write-HardDisk icon.”
From this discussion and the previous sections, we think
that it is feasible to bring the framework of argumentation inside
the concept blending process. Moreover, this appears to
work in a symmetric direction: the steps in an argumentation
process can be carried out through blending. For instance,
concept blending could be seen as the process behind the
creation of rational arguments (Coulson and Pascual, 2006).
One area closely akin to the icon domain is the domain
of sentences in a natural or artificial language. These can be
evaluated for their coherence, succinctness, and fitness-topurpose
from a semantic standpoint (including relationship
to other sentences), among other criteria; cf. (Abramsky and
Sadrzadeh, 2014) for a category-theoretic view.
Since people have different standards for evaluation, they
frequently disagree about what constitutes a satisfactory result,
be it a final outcome or a design decision that is only a
step the way to developing an artefact. They may also disagree
at a more fundamental level about what can be considered
a valid point of view or an appropriate manner of conducting
an argument. For example, “Godwin’s law” states
that an online discussion ends when someone compares one
of the discussants to Hitler and whoever made the comparison
automatically loses the debate. Naturally, the validity
of this principle is itself debatable. During the course of argumentation,
the goalposts may shift, as new information is
revealed about the domain under discussion, and about the
discussants themselves. The relationship between argumentation
and decision-making has been explored (Ouerdane,
2009), including the case of updating models of preferences
(Ouerdane et al., 2014); the latter is quite similar to our previous
work on Lakatos’s games (Pease et al., 2014).
Conclusion and Future Work
Computational models of combinatorial creativity faces the
daunting issue of evaluating a large number of possible
novel combinations. Particularly, Fauconnier and Turner
(1998) propose a model that includes a collection of optimality
principles to guide the construction of a ‘well-formed
integration network’. Our computational model, based on
generalisations of input spaces and amalgams, makes this
combinatorial nature more explicit. The heuristic criteria
called ‘optimality principles’ are too underspecified to be
used as computational measures to evaluate and select possible
blends. Moreover, alternative numeric measures may
be not enough to evaluate the quality or novelty of creative
artefacts. Our intuition is that in the context of creative outcomes,
people use argumentation to understand, criticise,
modify and evaluate them, and that computational argumentation
is a useful tool for computational creativity.
The domain of computer icons generated by blending,
where the evaluation of new icons is focused on their intended
meaning, shows that symbolic argumentation is a
process that is adequate to distinguish well-formed icons
from mix-and-match combinations, unambiguous and clear
icons from ambiguous or incomprehensible icons. This
domain supports our claim that numeric heuristic evaluation
measures are insufficient to recognise good blends, and
shows the usefulness of an argumentation-based process for
identifying good blends, detecting their critical problems,
and refining them in an evolving, open-ended process.
We have shown how Lakatosian reasoning can be used in
evaluating concept blending for icon design. Our approach
offers two main advantages. Firstly, the evaluation process
can improve the blend, since the dialogue about it refines
resulting blends. Secondly, the reasons behind a particular
evaluation are made explicit. This is crucial given recent
work on the importance of context in creativity judgments
(Charnley, Pease, and Colton, 2012; Colton, Pease, and
Charnley, 2011). Argumentation offers a framing story that
Proceedings of the Sixth International Conference on Computational Creativity June 2015 180
shows how and why a particular artefact was constructed,
which can be presented alongside the artefact itself.
We envision several future works. First, we intend to
specify an ontology for modelling the semiotic system presented
and to build a library of icons. Having a domain
knowledge will allow us to generate arguments by induction,
for instance, by analysing icons cases. Moreover, it will
also open the possibility to explore the use of value-based argumentation
(Bench-Capon, Doutre, and Dunne, 2002) for
selecting the input icons to be used in the concept blending
process. This latter point is important, since usually
the inputs of a blending process are assumed to be already
provided. Second, as far as the interpretation of icons is
concerned, we are thinking to take advantage of existing
approaches to natural language processing and understanding,
especially Construction Grammars (CxG). In CxG, the
grammatical construction is a pairing of form and content.
In our semiotic system, sign patterns seem equivalent to the
form, while interpretations would be akin to the content.
Working with a grammar would make evaluation more explicit,
e.g. we could use quantitative measures of ambiguity;
and this would open many other domains for application.
Finally, we plan to implement Lakatosian reasoning by
employing existing computational tools for argumentation
(Devereux and Reed, 2010; Wells and Reed, 2012). Our goal
is to provide a computational argumentation framework and
to integrate it into the framework for computational creativity
we are developing in the COINVENT project.
Acknowledgements
This work is partially supported by the COINVENT project
(FET-Open grant number: 611553).
<references_biblio/>
References
Abramsky, S., and Sadrzadeh, M. 2014. Semantic unification
– A sheaf theoretic approach to natural language. In
Categories and Types in Logic, Language, and Physics,
volume 8222 of LNCS, 1–13. Springer.
Bench-Capon, T. J. M., and Dunne, P. E. 2007. Argumentation
in artificial intelligence. Artificial Intelligence
171(10-15):619–641.
Bench-Capon, T. J. M.; Doutre, S.; and Dunne, P. E. 2002.
Value-based argumentation frameworks. In Artificial Intelligence,
444–453.
Boden, M. A. 2003. The Creative Mind - Myths and Mechanisms
(2nd ed.). Routledge.
Bou, F.; Eppe, M.; Plaza, E.; and Schorlemmer, M.
2014. D2.1: Reasoning with Amalgams. Technical
report, COINVENT Project. http://www.coinventproject.eu/fileadmin/publications/D2.1.pdf.
Chandler, D. 2004. Semiotics: The Basics. Routledge.
Charnley, J.; Pease, A.; and Colton, S. 2012. On the notion
of framing in computational creativiy. In Proc. of the 3rd
Int. Conf. on Computational Creativity, 77–81.
Colton, S.; Pease, A.; and Charnley, J. 2011. Computational
creativity theory: The FACE and IDEA descriptive
models. In 2nd Int. Conf. on Computational Creativity.
Coulson, S., and Pascual, E. 2006. For the sake of argument:
Mourning the unborn and reviving the dead through
conceptual blending. Ann. Rev. of Cognitive Linguistics
4:153–181.
Devereux, J., and Reed, C. 2010. Strategic argumentation in
rigorous persuasion dialogue. In ArgMAS, volume 6057
of LNCS. Springer Berlin Heidelberg. 94–113.
Dung, P. M. 1995. On the acceptability of arguments and its
fundamental role in nonmonotonic reasoning, logic programming
and n-person games. Artificial Intelligence
77(2):321 – 357.
Falomir, Z.; Cabedo, L. M.; Abril, L. G.; Escrig, M. T.; and
Ortega, J. A. 2012. A model for the qualitative description
of images based on visual and spatial features. Computer
Vision and Image Understanding 116(6):698–714.
Fauconnier, G., and Turner, M. 1998. Principles of conceptual
integration. In Koenig, J. P., ed., Discourse and Cognition:
Bridging the Gap. Center for the Study of Language
and Information. 269–283.
Lakatos, I. 1976. Proofs and refutations: the logic of mathematical
discovery. Cambridge University Press.
Ontan˜on, S., and Plaza, E. 2010. Amalgams: A formal ´
approach for combining multiple case solutions. In Proc.
of the Int. Conf. on Case Base Reasoning, volume 6176
of LNCS, 257–271. Springer.
Ouerdane, W.; Labreuche, C.; Maudet, N.; and Parsons, S.
2014. A dialogue game for recommendation with adaptive
preference models. Technical report, Ecole Centrale
Paris. Cahiers de recherche 2014-02.
Ouerdane, W. 2009. Multiple Criteria Decision Aiding: a
Dialectical Perspective. Ph.D. Dissertation, University
Paris-Dauphine, Paris, France.
Pease, A.; Budzynska, K.; Lawrence, J.; and Reed, C. 2014.
Lakatos Games for Mathematical Argument. In Proc.
of COMMA, volume 266 of Frontiers in Artificial Intelligence
and Applications, 59–66. IOS Press.
Prakken, H. 2005. Coherence and flexibility in dialogue
games for argumentation. J. Log. and Comput.
15(6):1009–1040.
Rahwan, I., and Simari, G. R. 2009. Argumentation in Arti-
ficial Intelligence. Springer Publishing Company.
Schorlemmer, M.; Smaill, A.; Kuhnberger, K.-U.; Kutz, O.; ¨
Colton, S.; Cambouropoulos, E.; and Pease, A. 2014.
Coinvent: Towards a computational concept invention
theory. In 5th Int. Conf. on Computational Creativity.
Walton, D., and Krabbe, E. C. W. 1995. Commitment in Dialogue:
Basic Concepts of Interpersonal Reasoning. State
University of New York Press.
Wells, S., and Reed, C. 2012. A domain specific language
for describing diverse systems of dialogue. Journal of
Applied Logic 10(4):309 – 329.
Proceedings of the Sixth International Conference on Computational Creativity June 2015 181