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Using Argumentation to Evaluate Concept Blends in Combinatorial Creativity Paper type: Study Paper 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 space, and selecting projections. Within the Concept Inven- tion Theory project1 (COINVENT), we are currently devel- This paper motivates the use of computational argumentation for evaluating ‘concept blends’ and other forms of combina- oping a computational account of concept blending based on torial creativity. We exemplify our approach in the domain of insights from psychology, Artificial Intelligence (AI), and computer icon design, where icons are understood as creative cognitive modelling (Schorlemmer et al., 2014). One of our artefacts generated through concept blending. We present a goals is to address this combinatorial nature. One potential semiotic system for representing icons, showing how they outcome of this work is a deeper understanding of the way can be described in terms of interpretations and how they combinatorial creativity works in general. are related by sign patterns. The interpretation of a sign pat- The formal and computational model for concept blend- tern conveys an intended meaning for an icon. This intended ing under development in COINVENT (Bou et al., 2014) is meaning is subjective, and depends on the way concept blend- closely related to the notion of an amalgam (Ontan˜on´ and ing for creating the icon is realised. We show how the in- tended meaning of icons can be discussed in an explicit and Plaza, 2010). Amalgamation has its root in case-based rea- social argumentation process modeled as a dialogue game, soning and focuses on the problem of combining solutions and show examples of these following the style of Lakatos coming from multiple cases. Assuming the solution space (1976). In this way, we are able to evaluate concept blends can be characterised as a generalisation space, the amal- through an open-ended and dynamic discussion in which con- gam operation combines input solutions into a new solution cept blends can be improved and the reasons behind a specific that contains as much information from the two inputs solu- evaluation are made explicit. In a closing discussion, we talk tions as possible. When input solutions cannot be combined, how argumentation can play a role at different stages of the amalgamation generalises them by omitting some of their concept blending process. specifics. This process of generalisation and combination is expensive from a computational point of view, because Introduction many alternatives have to be explored. A proposal by (Fauconnier and Turner, 1998) called concept The amalgam-based approach for computing blends blending has reinvigorated studies trying to unravel the gen- makes explicit the combinatorial nature of concept blending, eral cognitive principles operating during creative thought. which raises the issue of evaluating and selecting novel and According to (Fauconnier and Turner, 1998), concept blend- valuable blends as opposed to those combinations that lack ing is a cognitive process that serves a variety of cognitive interest or significance. Although Fauconnier and Turner purposes, including creativity. In this way of thinking, hu- (1998) suggest a number of qualitative criteria that can be man creativity can be modeled as a blending process that used for evaluating concept blends, it is difficult to charar- takes different mental spaces as input and blends them into acterise them from a computational point of view. a new mental space called a blend. This is a form of combi- In this paper, we propose to explore a discursive approach natorial creativity, one of the three forms of creativity iden- to understanding and evaluating the meaning, interest, and tified by Boden (2003). A blend is constructed by taking significance of concept blends. Specifically, we propose to the existing commonalities among the input mental spaces – view evaluating blends as a process of argumentation, in known as the generic space – into account, and by project- which the specifics of a blend are pinpointed and opened up ing the structure of the input spaces in a selective way. In as issues of discussion. Our intuition is that in the context of general the outcome can have an emergent structure arising new ideas, proposals, or artworks, people use critical discus- from a non-trivial combination of the projected parts. Differ- sion and argumentation to understand, absorb and evaluate. ent projections lead to different blends and different generic We also consider the constructive roles that argumentation spaces constrain the possible projections. can play in concept blending. This poses challenges from a computational perspective: Computational argumentation models have recently ap- large number of possible combinations exhibiting vastly dif- peared in AI applications (Bench-Capon and Dunne, 2007; ferent properties can be constructed by choosing different input spaces, using different ways to compute the generic 1See http://www.coinvent-project.eu for details. G 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 I1 I¯2 designers who create new icons by blending existing icons and signs. To this end, we propose a simple semiotic sys- tem for modeling computer icons. We consider computer I1 B I2 icons as combinations of signs (e.g. document, magnifying glass, arrow etc.) that are described in terms of interpre- Figure 1: An amalgam diagram with inputs I1 and I2 and tations. Interpretations convey actions-in-the-world or con- ¯ ¯ blend B obtained by combining I1 and I2. The arrows indi- cepts and are associated with shapes. Signs are related by cate generalisation. sign-patterns modeled as qualitative spatial relations such Rahwan and Simari, 2009), and we believe that incorpo- as above, behind, etc. Since sign-patterns are used to com- rating argumentation can foster the development of a fuller bine signs, and each sign can have multiple interpretations, computational account of combinatorial creativity. The cur- a sign-pattern used to generate a computer icon can convey rent paper develops these themes at the level of (meta-) multiple intended meanings to the icon. These are subjec- design; implementation is saved for future work. tive interpretations of designers when they have to decide what is the best interpretation an icon can have in the real Roles of Argumentation in Concept Blending world. In this paper, we show how the intended meaning of Consider the amalgam diagram modeling the concept blend- new designed (blended) icons can be evaluated and refined by means of Lakatosian reasoning. ing process (Figure 1): two input spaces I1, I2, two of their ¯ ¯ possible generalisations I1, I2, which have a generic space Background G and blend B. When two input spaces cannot be combined because they do not satisfy certain criteria, the inputs have Computational Argumentation to be generalised for omitting some of their specifics. The Computational argumentation in AI aims at modeling the combination of each specific pair I¯1, I¯2 yields a blend. constitutive elements of argumentation, that are i) argu- Informally, we can imagine argumentation taking place ments, ii) attack relations modeling conflicts, and iii) ac- at various points in the amalgam diagram. In general this ceptibility semantics for selecting valid arguments (Bench- would happen in response to indeterminacy, that is, when Capon and Dunne, 2007; Rahwan and Simari, 2009). some features of the diagram are underdetermined. We fore- The most well-known computational argumentation see that argumentation can be used: framework is due to Dung (1995). Dung defines an abstract a. to express opinions or points of view that can be used for framework to represent arguments and binary attack rela- guiding the selection/omission of specific parts of the in- tions, modeling conflicts, by means of a graph. He defines put spaces; in particular, to select a specific pair of gener- different acceptibility semantics to decide which arguments alisation I¯ , I¯ of the input spaces in the blending process; are valid and, consequently, how conflicts can be resolved 1 2 (Figure 2). b. to provide a computational setting for modeling discus- sions around the quality of a creative artefact, with the aim of evaluating and refining the generated blends. a a a In the first case, arguments would be about generalisation, 1 2 3 i.e. which features should be preserved from I1 and which features should be preserved from I . More complex infer- 2 Figure 2: Dung framework example: Nodes represent argu- ences could be involved, for example in a case where I1 is fixed, and constraints and various optimality criteria on the ments and edges (binary) attack relations. Argument a1 is blend are imposed, which then yield various constraints on attacked by a2 which is attacked by a3. Thus, a2 is defeated and a1 can be accepted. a3 is also accepted. 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 Abstract argumentation frameworks do not deal with how second case. arguments are generated and exchanged. They merely fo-

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