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Using Argumentation to Evaluate Concept Blends in Combinatorial Creativity

Authors: Confalonieri R.; Corneli J.; Pease A.; Plaza E.; Schorlemmer M.;

Using Argumentation to Evaluate Concept Blends in Combinatorial Creativity

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 a closing discussion, we talk how argumentation can play a role at different stages of the concept blending process.

This work is partially supported by the COINVENT project (FET-Open grant number: 611553)

Peer Reviewed

Countries
Italy, United Kingdom
Keywords

Concept blends, 100, Semiotic system, 004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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