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Item type:Publication, Access status: without files , Upward refinement operators for conceptual blending in the description logic 𝓔𝓛⁺⁺(2018-03-01); ; ; ; Conceptual blending is a mental process that serves a variety of cognitive purposes, including human creativity. In this line of thinking, human creativity is modeled as a process that takes different mental spaces as input and combines them into a new mental space, called a blend. According to this form of combinational creativity, a blend is constructed by taking the commonalities among the input mental spaces into account, to form a so-called generic space, and by projecting the non-common structure of the input spaces in a selective way to the novel blended space. Since input spaces for interesting blends are often initially incompatible, a generalisation step is needed before they can be blended. In this paper, we apply this idea to blend input spaces specified in the description logic + + and propose an upward refinement operator for generalising + + concepts. We show how the generalisation operator is translated to Answer Set Programming (ASP) in order to implement a search process that finds possible generalisations of input concepts. The generalisations obtained by the ASP process are used in a conceptual blending algorithm that generates and evaluates possible combinations of blends. We exemplify our approach in the domain of computer icons.Publicationtype: Journal Article14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A computational framework for conceptual blending(Elsevier, 2017-12-02); ; ; ; We present a computational framework for conceptual blending, a concept invention method that is advocated in cognitive science as a fundamental and uniquely human engine for creative thinking. Our framework treats a crucial part of the blending process, namely the generalisation of input concepts, as a search problem that is solved by means of modern answer set programming methods to find commonalities among input concepts. We also address the problem of pruning the space of possible blends by introducing metrics that capture most of the so-called optimality principles, described in the cognitive science literature as guidelines to produce meaningful and serendipitous blends. As a proof of concept, we demonstrate how our system invents novel concepts and theories in domains where creativity is crucial, namely mathematics and music.Publicationtype: Journal ArticleScopus© Citations 63 69 258 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Conceptual blending in EL⁺⁺(RWTH Aachen, 2016); ; ; ; The cognitive theory of conceptual blending models human creativity as a mental process that combines two mental spaces into a new mental space, called a blend. According to this theory, a blend is constructed by taking the commonalities among the input mental spaces into account, to form a so-called generic space, and by projecting their non-common structure in a selective way to the novel blended space. In this paper, we apply this idea to blend input spaces modeled as complex EL⁺⁺ concepts. To construct the generic space of two EL⁺⁺ concepts, these need to be generalised in a controlled manner. For this, we propose an upward refinement operator that is used for finding common generalisations of EL⁺⁺ concepts.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , ASP, Amalgamation, and the conceptual blending workflowWe present a framework for conceptual blending – a concept invention method that is advocated in cognitive science as a fundamental, and uniquely human engine for creative thinking. Herein, we employ the search capabilities of ASP to find commonalities among input concepts as part of the blending process, and we show how our approach fits within a generalised conceptual blending workflow. Specifically, we orchestrate ASP with imperative Python programming, to query external tools for theorem proving and colimit computation. We exemplify our approach with an example of creativity in mathematics.Publicationtype: Conference Paper55 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Upward refinement for conceptual blending in description logic - An ASP-based Approach and case study in ε葦⁺⁺Conceptual blending is understood to be a process that serves a variety of cognitive purposes, including creativity, and has been highly influential in cognitive linguistics. In this line of thinking, human creativity is modeled as a blending process that takes different mental spaces as input and combines them into a new mental space, called a blend. According to this form of combinatorial creativity, a blend is constructed by taking the existing commonalities among the input mental spaces-known as the generic space-into account, and by projecting their structure in a selective way. Since input spaces for interesting blends are often initially incompatible, a generalisation step is needed before they can be blended. In this paper, we apply this idea to blend input spaces specified in the description logic ε葦⁺⁺ and propose an upward refinement operator for generalising ε葦⁺⁺ concepts. We show how the generalisation operator is translated to Answer Set Programming (ASP) in order to implement a search process that finds possible generalisations of input concepts. We exemplify our approach in the domain of computer icons.Publicationtype: Conference Paper57